{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate forecasting\n",
    "> Tutorial on how to do multivariate forecasting using TSMixer models."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In _multivariate_ forecasting, we use the information from every time series to produce all forecasts for all time series jointly. In contrast, in _univariate_ forecasting we only consider the information from every individual time series and produce forecasts for every time series separately. Multivariate forecasting methods thus use more information to produce every forecast, and thus should be able to provide better forecasting results. However, multivariate forecasting methods also scale with the number of time series, which means these methods are commonly less well suited for large-scale problems (i.e. forecasting many, many time series).\n",
    "\n",
    "In this notebook, we will demonstrate the performance of a state-of-the-art multivariate forecasting architecture `TSMixer` / `TSMixerx` when compared to a univariate forecasting method (`NHITS`) and a simple MLP-based multivariate method (`MLPMultivariate`).\n",
    "\n",
    "We will show how to:\n",
    "* Load the [ETTm2](https://github.com/zhouhaoyi/ETDataset) benchmark dataset, used in the academic literature.\n",
    "* Train a `TSMixer`, `TSMixerx` and `MLPMultivariate` model\n",
    "* Forecast the test set\n",
    "* Optimize the hyperparameters"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/LongHorizon_with_Transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast datasetsforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load ETTm2 Data\n",
    "\n",
    "The `LongHorizon` class will automatically download the complete ETTm2 dataset and process it.\n",
    "\n",
    "It return three Dataframes: `Y_df` contains the values for the target variables, `X_df` contains exogenous calendar features and `S_df` contains static features for each time-series (none for ETTm2). For this example we will use `Y_df` and `X_df`. \n",
    "\n",
    "In `TSMixerx`, we can make use of the additional exogenous features contained in `X_df`. In `TSMixer`, there is _no_ support for exogenous features. Hence, if you want to use exogenous features, you should use `TSMixerx`. \n",
    "\n",
    "If you want to use your own data just replace `Y_df` and `X_df`. Be sure to use a long format and make sure to have a similar structure as our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from datasetsforecast.long_horizon import LongHorizon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change this to your own data to try the model\n",
    "Y_df, X_df, _ = LongHorizon.load(directory='./', group='ETTm2')\n",
    "Y_df['ds'] = pd.to_datetime(Y_df['ds'])\n",
    "\n",
    "# X_df contains the exogenous features, which we add to Y_df\n",
    "X_df['ds'] = pd.to_datetime(X_df['ds'])\n",
    "Y_df = Y_df.merge(X_df, on=['unique_id', 'ds'], how='left')\n",
    "\n",
    "# We make validation and test splits\n",
    "n_time = len(Y_df.ds.unique())\n",
    "val_size = int(.2 * n_time)\n",
    "test_size = int(.2 * n_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>ex_1</th>\n",
       "      <th>ex_2</th>\n",
       "      <th>ex_3</th>\n",
       "      <th>ex_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:00:00</td>\n",
       "      <td>-0.041413</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:15:00</td>\n",
       "      <td>-0.185467</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:30:00</td>\n",
       "      <td>-0.257495</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 00:45:00</td>\n",
       "      <td>-0.577510</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HUFL</td>\n",
       "      <td>2016-07-01 01:00:00</td>\n",
       "      <td>-0.385501</td>\n",
       "      <td>-0.456522</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>-0.001370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403195</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 22:45:00</td>\n",
       "      <td>-1.581325</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403196</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:00:00</td>\n",
       "      <td>-1.581325</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403197</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:15:00</td>\n",
       "      <td>-1.581325</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403198</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:30:00</td>\n",
       "      <td>-1.562328</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403199</th>\n",
       "      <td>OT</td>\n",
       "      <td>2018-02-20 23:45:00</td>\n",
       "      <td>-1.562328</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.363014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>403200 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       unique_id                  ds         y      ex_1      ex_2      ex_3  \\\n",
       "0           HUFL 2016-07-01 00:00:00 -0.041413 -0.500000  0.166667 -0.500000   \n",
       "1           HUFL 2016-07-01 00:15:00 -0.185467 -0.500000  0.166667 -0.500000   \n",
       "2           HUFL 2016-07-01 00:30:00 -0.257495 -0.500000  0.166667 -0.500000   \n",
       "3           HUFL 2016-07-01 00:45:00 -0.577510 -0.500000  0.166667 -0.500000   \n",
       "4           HUFL 2016-07-01 01:00:00 -0.385501 -0.456522  0.166667 -0.500000   \n",
       "...          ...                 ...       ...       ...       ...       ...   \n",
       "403195        OT 2018-02-20 22:45:00 -1.581325  0.456522 -0.333333  0.133333   \n",
       "403196        OT 2018-02-20 23:00:00 -1.581325  0.500000 -0.333333  0.133333   \n",
       "403197        OT 2018-02-20 23:15:00 -1.581325  0.500000 -0.333333  0.133333   \n",
       "403198        OT 2018-02-20 23:30:00 -1.562328  0.500000 -0.333333  0.133333   \n",
       "403199        OT 2018-02-20 23:45:00 -1.562328  0.500000 -0.333333  0.133333   \n",
       "\n",
       "            ex_4  \n",
       "0      -0.001370  \n",
       "1      -0.001370  \n",
       "2      -0.001370  \n",
       "3      -0.001370  \n",
       "4      -0.001370  \n",
       "...          ...  \n",
       "403195 -0.363014  \n",
       "403196 -0.363014  \n",
       "403197 -0.363014  \n",
       "403198 -0.363014  \n",
       "403199 -0.363014  \n",
       "\n",
       "[403200 rows x 7 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Train models\n",
    "\n",
    "We will train models using the `cross_validation` method, which allows users to automatically simulate multiple historic forecasts (in the test set).\n",
    "\n",
    "The `cross_validation` method will use the validation set for hyperparameter selection and early stopping, and will then produce the forecasts for the test set.\n",
    "\n",
    "First, instantiate each model in the `models` list, specifying the `horizon`, `input_size`, and training iterations. In this notebook, we compare against the univariate `NHITS` and multivariate `MLPMultivariate` models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%capture\n",
    "from neuralforecast.core import NeuralForecast\n",
    "from neuralforecast.models import TSMixer, TSMixerx, NHITS, MLPMultivariate\n",
    "from neuralforecast.losses.pytorch import MSE, MAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lightning_fabric.utilities.seed:Seed set to 12345678\n",
      "INFO:lightning_fabric.utilities.seed:Seed set to 12345678\n",
      "INFO:lightning_fabric.utilities.seed:Seed set to 12345678\n",
      "INFO:lightning_fabric.utilities.seed:Seed set to 12345678\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "horizon = 96\n",
    "input_size = 512\n",
    "models = [\n",
    "          TSMixer(h=horizon,\n",
    "                input_size=input_size,\n",
    "                n_series=7,\n",
    "                max_steps=1000,\n",
    "                val_check_steps=100,\n",
    "                early_stop_patience_steps=5,\n",
    "                scaler_type='identity',\n",
    "                valid_loss=MAE(),\n",
    "                random_seed=12345678,\n",
    "                ),  \n",
    "          TSMixerx(h=horizon,\n",
    "                input_size=input_size,\n",
    "                n_series=7,\n",
    "                max_steps=1000,\n",
    "                val_check_steps=100,\n",
    "                early_stop_patience_steps=5,\n",
    "                scaler_type='identity',\n",
    "                dropout=0.7,\n",
    "                valid_loss=MAE(),\n",
    "                random_seed=12345678,\n",
    "                futr_exog_list=['ex_1', 'ex_2', 'ex_3', 'ex_4'],\n",
    "                ),\n",
    "          MLPMultivariate(h=horizon,\n",
    "                input_size=input_size,\n",
    "                n_series=7,\n",
    "                max_steps=1000,\n",
    "                val_check_steps=100,\n",
    "                early_stop_patience_steps=5,\n",
    "                scaler_type='standard',\n",
    "                hidden_size=256,\n",
    "                valid_loss=MAE(),\n",
    "                random_seed=12345678,\n",
    "                ),                                             \n",
    "           NHITS(h=horizon,\n",
    "                input_size=horizon,\n",
    "                max_steps=1000,\n",
    "                val_check_steps=100,\n",
    "                early_stop_patience_steps=5,\n",
    "                scaler_type='robust',\n",
    "                valid_loss=MAE(),\n",
    "                random_seed=12345678,\n",
    "                ),                                                                       \n",
    "         ]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "Check our `auto` models for automatic hyperparameter optimization, and see the end of this tutorial for an example of hyperparameter tuning.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instantiate a `NeuralForecast` object with the following required parameters:\n",
    "\n",
    "* `models`: a list of models.\n",
    "\n",
    "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n",
    "\n",
    "Second, use the `cross_validation` method, specifying the dataset (`Y_df`), validation size and test size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(\n",
    "    models=models,\n",
    "    freq='15min')\n",
    "\n",
    "Y_hat_df = nf.cross_validation(df=Y_df,\n",
    "                               val_size=val_size,\n",
    "                               test_size=test_size,\n",
    "                               n_windows=None\n",
    "                               )                                 \n",
    "Y_hat_df = Y_hat_df.reset_index()                               "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `cross_validation` method will return the forecasts for each model on the test set."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Evaluate Results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we plot the forecasts on the test set for the `OT` variable for all models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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izFOHZ/DfPn03vnjv0/P+bN0YY77/TLs1fEROiCMRhiAIgiAIgiAIgiAIIgM5YYhhQCIMsaCENRcqngv+5x0aRdzxr9vxL+++C0GvOl0YAOw4NGteT1qumCIiLuBZKb364fJKR8YtwSuIqwWkOPf32X6/tK2MY0jmZZYtp3RkGXVqDnzo5qfwjUcO4drPPzDvr9r/5GR1g1weUcmrj9NCIkmEIQiCIAiCIAiCIAiCqEYsXuyGWDmQCEMMHbusS1AjquSdA3GN+0RKiXu/sRvH9s3i4e/tq+2LsL6/rsZLGAt4dt/nmY5M1rh+FhIpJSKrP3X7PYqy2/bQoYerVg7hZFNXLbbgNB9YozGv9sc6qVhXV6NIhCEkSy+jux48Wtk+P3sienr+bpuhkRNhZIUQRxAEQRAEQRAEQRAEcSqSn8RKEM8EEmGIoSOswHVQM9M/X1ukLn3Zfiu92J5Dndq+SEssCGtElTAWsJNFzVeEWUx3SJTrS11NmCBXhL3yMEk54IRZ7Po382G+IozvppfFuv0oOh0IJx01qzeOVK88N3ti5qab5tW3YZJ34YgeiTAEQRAEQRAEQRAEQRAZCmJgdZN2CSIPiTDE0Imsi9N8nTB1bpITR1MRZv/kHNInWX2ZPFAt2gSxgCfT1FXHnpiqXnXuIswX0R0S5fpSXxMm6woSvDxllyxywsTL58fG27jRvBZBUNve3hN141eGEYSTClR+061oPeiEaV18cW1/FowBJwylIyMIgiAIgiAIgiAIgrCRRenISIQh5gmJMMRQ4ULC1lFqa8LkxII6J8yM5YSZS0os2wnDay6QIRew/R4cNamoBpwwi3cBjgecMPOrCSPiiropQixrJ0z7ec8zr8XsbEXLhMgag72oJu9nHGWcMPnxnEfyrPgVHzte25+FYsAJQ+nICIIgCIIgCIIgCIIgshTFKilFGTFPSIQhhkregVHnJBB5J0yNkDE7ldbrqAt4J43S9cU1fcnXhBE1Z8dSqgmTF7vq0mjFcVYM4FXlcgqcMLxG5FmqzEWE6VvCS50II6OsCFMrxOWO0/7/9b9q+7NQRHv2ZN6LLjlhCIIgCIIgCIIgCIIgMhQ5YUiEIeYJiTDEUMmLAfVOmPnVhOl101omcxFhmNWkToQJIg7PSkYVhTVCRi5Av7g1YeaZjizO1oRh0itpmYhLIueEyTtpljQy3ReiU19HKLBFmLCmLlAUQbhpzZnaMVn0w71EoHRkBEEQBEEQBEEQBEEQWfKp5QGqCUPMHxJhiKEy6ISpDjoPpCOrcRJ88rad5vXeo93a/jjzEGHCIJcqqqZ9FCTbJpC0W0o1YerSkfWimcz7OKrqu4TMOWHyNWWWMvaPJZ+DE8Z2v/Rr9iPvZ8Ws+nRkS2emhMzN2hA9SkdGEARBEARBEARBEASRocj1Qk4YYp6QCEMMlQFHxjxrwtSl9PJl6lQZa5S7NzSupUzzOhEm53qIawLqQZAE4EM3cRD0gsULYg+KMHViQLYGTKWzRQgIlktHtoxEGPuHUczWO2H61jjp1zhh4n6Yec/rasgsJSdM7lwT5IQhCIIgCIIgCIIgCILIIAtiOUtpki2xPCARhhgqA2JAjfDRD3IpvWpFGKvtHNKRuVb7OpdClOtLXft+mIgusZsE4ndN7q7tz0IR5Vw4dSJMPnVapeAkBGROhMmnYlvKyHmmI+vNoyZMnHdP9cKSlqovObFr7CdeWdufBUPmBNCwuu8EQRAEQRAEQRAEQRCnHEWCiyQRhpgfJMIQQyWfjqzOCRPkgth16ch867WcQ/ovdx6iTb4GTK1oo4rbb5hUKakW8fo7fydMdt9VpVKTUkI6ieuIKfU/DqPS9ksOPj8Rpj8fESaXjqz7yKOV7fXsib4ayLLdqu3PQpFPRyaXwDF97I4D2P3wscXuBkEQBEEQBEEQBEEQREKR4ELpyIh5QiIMMVTyoktdbZIg5z6pS0c23kyD6EJUp/8SXMBBmnZLxtXrHqgJE1en3NJuEkdoEYZVtAbCPXuw7bkX4bHnPR+SD9dJMuhAql5/fj9XpiOT0qQjc3kAYHmlI5PWcZyL28N2b3Xr0pHlxkx/+67K9kL1JVbGoun+ZG1/Fozc6bDYTpgTBzv41qe24d8+9MCi9oMgCIIgCIIgCIIgCEJTlHosP7GVIOpYdiLMhz/8YZxzzjlotVq48sorcdttt5W2veWWW8AYG/j32GOPncQen1rk02LlnTF5gnB+6cggA/PSdavTf8W5VGj5FFx5eC6gfqT/ZHVXlHChRZg6J+L2q1+dtIsizN5yS3XjeRLmBKY6BxLLmR4qHUhCQLgNAIDLVQq25STCWOKCjKqFBi5kZt/N9Ku3Mw6yO1LIaiGO86Q911deWe/mWjByNwyd75ZfS08Gs8fTc3s5pbsjCIIgCIIgiJOBjCIcfNe7MXPzzYvdFYIgiFOLovq+ixnPIZYly0qE+dznPoc3v/nNeMc73oH77rsPL33pS/Ga17wGe/bsqfzc448/jgMHDph/559//knq8alHXnSpS4sVhnmhpPoixkQ6ZMeCNZVteU6EieJuZfv27JHM+9n4YGV7TCWuHFfMPx2ZmJ2de+M5MN9aPG6YWDEawRQAgFeJNlIidpO0WX6U9HtZOWGiVCgRQVDRMpuKDACme9UpuuIwux/kyHh1+yhA7DZxfM2l4I63uDMnBmrCLG46Mlt4CXskwhAEQRAEQRCEzd43/Q5OfPazePpNv7PYXSEIgjilKIzdkBOGmCfLSoT567/+a/zWb/0W3vCGN+Ciiy7C+9//fmzduhUf+chHKj+3ceNGnHbaaeaf67qV7YlnTl4MqHfC5ILYFU4YKSUaMm3PwCrXH+ZSnXFZk24pygboHVFzenQC1W5u6chs3HXr59x2Lsy3JowWjDzlbKnKjsbj1AnjKyGLLyOngi3C1KXcyu/Hfk06vTg3xtzTNle2D6MAj134X7Dz/P+OHef8zKLOnNA3Ec6qVQAA0a0WKRea3mx6nOrqMREEQRAEQRDEqUanIgsIQRAEsYBQOjJiCHiL3YG5EoYh7rnnHrztbW/LLL/66qtx++23V372iiuuQL/fx8UXX4z//b//N17+8peXtg2CAIE1W356ehoAEEURomjxC1cvdXpBNsjdDar3W5BLARZFcWn7iAusQs+8d4WH47M9rBttFLafnc3WjHEEq+wLz6VGc6Rb2V5KXRMm2QYeizmPkTgIntF40p/Jf7YfDr6vWj9TvxWOqvEiKvre6aauHUeJNlHNcV1KCEt44b1eZb8Hx2/5eASS/QA0zfuYy8r24hu34PCP/QIAYO/WV+Js/v8WbT9qNxNzE7GRtdtDHZPzpTOZnq9BL0QrIrGcWHoMa7wTxDCg8UgQ5dD5QSwlFmI80tgmVgp0vSaWEmXjkceD4zMOQ4DG7SnPfK5dy0aEOXr0KDjn2LRpU2b5pk2bcPBgcdqozZs342Mf+xiuvPJKBEGAT3/603jlK1+JW265BS972csKP/Oe97wH73znOweW33jjjRgZGXn2G7LCefA4A5AGTx997Anc0CmvwXPsSAtt+Gn7Rx7F3m5xYe5+DDCZrtuVPr5x4zexulnYHLOTDoBR894RHm644YbSvsyemEQbq631V7cXRoRJAveTJ6Yq219gvb7v29/G9OxMads6brrppsz7e49m9/uTO3bhhht2lH5eqmuEy5O+dzvd0r6Pfud2AEk9G0e137ljJ6ZvOPwMe39yOevYUSOT7HzySdxVcYwmA8C+LD5Vsx/x5E4Azzdvpw8eqB4DlvOFiQjHjx2rbL+QrHroIWwCEMQcHoDO7Oyz6kt+TM6XyceaABJB9ZZv3wp/nGaVEEuXZzveCWKY0HgkiHLo/CCWEs92PNrPk4v1DEEQCwVdr4mlRH48rn9qO9YCmHzRj2D1nd8HANz8rW8hXr365HeOWFJ055FVZtmIMBrGsimfpJQDyzQXXnghLrzwQvP+qquuwt69e/G+972vVIR5+9vfjmuvvda8n56extatW3H11VdjYmJiCFuwwnnoIPD4g+btOeeeh2teVV6D55MPfh/hidR9cOEFz8Xlr9pavOp9U9hx29fMe1d4eMmPvRhnrS0Wx7ZvO45v3fGIec8kcM0115T25cn7rwMm0/fjfKKy/SdvuB4A4Khi6+Nj47jmmqtL2z/1B6mL6+LTTsPainVrDu+egeACp52bpIyKogg33XQTXvWqV8H3U/EqvH8/8OTD5v2mLWfgmmsuLV3vE9/6BgDAVQJSq9ku3dadd96Hx5PyN3BUSrfTt5yBV1xzVW3/lwK7P/p3ePS8/4DOyGa85LQj+KGK/b73RBe497vm/cbNp+Oaa55X2v6B7V/G05aW1uiLyjFjjwEv7mPN6tV4yRzGwUJwbNcunADQkBICwEirVdn3MsrG5Hy55djjeGJnIuy9+KqXYMOZ1fV1CGIxGNZ4J4hhQOORIMqh84NYSgxrPNrPEs/kvp0gliJ0vSaWEmXj8chDD2Pqtttw9iWXYlKJMC+7/HK0Lr54sbpKLBF0Bq25sGxEmPXr18N13QHXy+HDhwfcMVW86EUvwmc+85nSvzebTTSbg9YK3/fpB2EOcLCB91X7zT92DCHGkyLhzAFjTmn7WDpwrPU70oNEeXuuarS4cR/ca8GVXmVfvDibisoX1X2XqqSSq9KRNZ3W3MfI7GxtW84FvvS++wEAb/j/vQzNdnq65sejyO33SKBy/Y5U+0alI2Oyor2fnA+OiABdk6dm/Q/f+jTuvmEXfup3Llv0YHoU9rD3/J8AABzsPYAzq/Y7y6bAioSs3M44zqbTE071GLPx4h7cffsX7bpy4iMfBQCIGaUiCfGs+vJsr5FhL00HyODS9ZZY0tA9AbGUoPFIEOXQ+UEsJYY5Hp0ghDs2Wt+QIJYJdL0mlhL58agrRrt+Gpc78Tcfwpn/8PcnuWfEUmM+162ayuNLh0ajgSuvvHLAEnbTTTfhxS9+8ZzXc99992Hz5uri2cQzJ1/YPKwpsK3rWGkxQIryQuX9iEPIdHC70kUvV1Mm893qbx5P6si4woGsKIT+nO5DANI6KY4odlileKpd4oSR86hVP/WVr9S2iaygdNCpzjEY8ux2hTUF5R2hBKQ57Hd21lnJ/yIGkKyXR9Xrv/Wfn0BnKsQjt+2rbHcymLbqvIia/RLn9kM/qh6/US9ZN1NjQLDqOibcSX+weyMb4e47VNn+pLLIReWiID02vOa6QRAEQRDLHT47iwN/+qfo3n33YneFIIhlCD92dLG7QBAEceqg4iXMSWM+8ZEji9UbYpmybJwwAHDttdfiV3/1V/HCF74QV111FT72sY9hz549eOMb3wggSSW2b98+/OM//iMA4P3vfz/OPvtsXHLJJQjDEJ/5zGdw/fXX4/rrr1/MzVjR5MWAvCiTRxpHRgjutSF4uRgQxAKOyA7ZTj8saZ2KMG7cT2qnMwdSSDC3WFyZiKYAJA6F0G3Cjas1SslyIkyFkAEA7SuuQO+++wAA/PjxyrYAMHMiLVRetV8AIMoFrYOaIDYz+12JOxXN5cRq9ZlUhBEV67eFrkO75m7LWygCtM3rKCoX7YDB8dqvEZvifrL/vLiPqOFDOtWX1N7mMzPvq4/qyUUOWYTpRxxv/Mw9uHjzBN76k8+tbW8LLyTCEARBECudIx/4ICb/5XOY/JfP4aLHti12dwiCWGbImudsgiAIYnhIoWJDThonlEGwSL0hlivLSoT5pV/6JRw7dgzvete7cODAAVx66aW44YYbcJaaqX/gwAHs2bPHtA/DEG95y1uwb98+tNttXHLJJfja175G+VMXkLzzJa4RD7ypKWBkzNQmERVCRhgLjEbZ1Fa9ChEmOrwXANCIZqHLJAku4ZSYFfa6yTjy4j7CZuoWKUOqFGBahBF1TpgKF04RM8dSESYK6xwcyX5vuA5CLhDUODgcmXXCVIkwsXKPSMSQcxFhotS1M3ts7gWqFoqIpTZ9XjMe8+O1TsyKlUjj8T4ijEOw6ktqxFqZ98LxITodOKNLIJXAkEWY7zxxBLc8nvx7y9UXwnGqnWU8Tvc9rxm/BEEQBLHcCXfsWOwuEASxjLGfuQiCIIgFRgnfzLVEmLA8HkkQRSwrEQYA3vSmN+FNb3pT4d8++clPZt6/9a1vxVvf+taT0CtCk3cS1DlhoEQXhycXryo3SSwEmPRglz/pVijP8fFjAMbQCKbMMs5l6aAPZRIg1+nLmKzJ1icdgM3dCSPl/ALLQTd1bdQ6YdTfx1oejndChDX7XYswjpiDCKMD4jKGRNKnqrRetqMi7M8jR9sCETXWm9cHJw9Uto3FPJ0wSqRx42TMiDKFT8GRzRUZe21MfeUrWPMrv1L5uZMCH+6x6ln77qkjs7hgU3VtINv90puhmxmCIAhiZdP53vcWuwsEQSxjKPhHEARx8jDxPCvmM+xsIsTKZ9nUhCGWB/m0WHVigFDpm7Qjo8oJE0UCYNkh2w/LZwA1996ZWTdQ7eCArpMSJw4UR1QH1LUTxjU1YWqcLvO0jNt1V+pEGO1AGmm4mfdFCCHBoJ0wyc17qyqtm3HhxJBMO2HK+9ObSve335us7LeGRwK3X/8U9j1xYk7tgTkIfArppOnIGr2aGi/zdcIonczjffVd5bq2lBLoZYUO4fhD/eGWnKO/bduc1jly1YsAAI9uVZ8d8g2E7cZ6ZP9URcsEEmEITVXtLoIgiJUIXfcIgpgvx3MTUAmCIIgFRMefajJ8EEQVJMIQQ0WLLq66MNWlI9Ppm7SQwSuC+3GB+yKoEGF63REAgCNjMKnEg4r+SOUO0QF1JqtFGIhsOjLWrQkcW0Fud/36ioYJ9r4QNYKDFiTavpt5X9gNK9itBSpdI6aIXqC2T8YQc0hHZgfQ6wrVax749l7cd9MefOmv75tT+9d95oO4/K//Ag89XR3cl5xDOqn7xJHV5r/8eK1zwmjziKeEu6p0ZGJmBtxtZpc5HpgzvMvwoT//c+z8Dz+Pox/+SG1bd9VqAMDOTcmxH3Y+0+l+em52a9LpAVkRJuwtvoOKWBy+8Q8P41/efRelpCMI4tSC0goRBDFPpr/2tcXuAkEQxKmDqj/A7OwnNImGmCckwhBDRYsw2pFR51bIO2HiivaRFZRzVPugwobdF2uStiIGUxfMqCKo7qjnX0+llmKoccIwndJLfTCoDhzbsxznksPXDkpXOYSAdD/r/R5XtLfromgnDBPlIkw/tNKRsXoRJrL2A3dbpe1sDu+anlM7AJgJZ/AE/3s4Gz+HL9y3vbKtjCIzxoB6YS1SQpmnRMR+TSBYizAnRpQIU5GOTMZxgQgzXCfMic/+MwDg6Ic+VNtWqs7PtpUIE4YQnc7Q+jIbpOn0enMQYWx3VV0NJGJlEoUcT919GMf3d3Bs/+xid4cgCOKkISitEEEQBEEQxJLFlB+wasKQCEPMFxJhiKGi02CNNpLAd106Mp2+yRHJw2eVSBJHg+JBGMVlzcGVsMAkN06YoCK42z6iiqzHc3PCGBGGq3RkNdffaM+e9LPzFWHmWBOmrUWYuTph1H5nFauPe139KhVhKvoT9a1aNm4DfA5pw3Y/dKy2jaavjg8AxLx6P8owhMg4YdzKlB/aCTPaTMZlUFH7BkgdqaGna8L4pW1lFBU6YRarqKaMk++dHEuX8dnhiTB2TZj5OmEq0wYSK5b+bHouMEY2b4IgTh2G7UYlCIIgCIIghgjXThgKoxPPHBo9xFDpKhfE6pEkGF3rhNHpyIyoUt5eB2kdHsKRSaC/ygmjhWpHcDgiad+vcKvEzabqS08tqXA1SAmoVFvaCSMr3CQAIDodRF4bkxPnQsxFhInmI8JoJ4yn3lc4YdR+ZJZDqFKE0ftYcrhqv1f1Jw6ywljcLxfKNOPr5uaYAYBYpMdQonq/JE4YSxhhXlrIpQC9H8e0CFPnhFF1hGJH1YSpSL8moxhxkRNmkUSYeGoSANBtArEaNzIa3kxce9/NV4SpSktIrFyCbnoucBLiCII4hSARhiAIgiAIYuliMphQOjLiWUAiDDFUpnpJEG3dWAPAHGrCOFkRJqhywqhArp1eLK6oCaNj9cyqCVPlhAnbSZ+NEwYVRdatdF9a4JEVp5N2X9z9grfi3hf8fzg6cVFtEdasE6Y6IKkdSMYJU5HiSgsotkOoqiZMpEQLx3LCyIrjGuaErmi2V9IyxW/OrXYMANz82CHzWshqgSefjkw4bqXooUWY8Vbq5OIVqd30IYyUCAPmlB+ruMAJwzz4p51WuQ0LBZ9KUsDNtAHhJ2NXDjEdSj/jhKkX4ux0ZHNxTxErj6CbjpOYUtIRBLEMEf1+faOiz5EIQxAEQRAEsXRRMTZmpSOb+OmfXqzeEMsUEmGIoaKLca8bTYLNVU4YKeRAOjJe5eCItAgTwVHiQVw1cz/SacW4aV8lwkjlfNE1YcDc0tnY0lqsU3rp9GSFKCGjN7IRAHB44wsqHRnA/JwwHRXkXt3WDqTy9rq+DJPC7BdWlaLL7AOBwEnah3G5kNGdyh6TeLZb0jJlPhMI/uXuHembKgsPBp0wBzf9cKUIk09HBqQCVxFa6wrdVGgqG8PFNWE8OCMj5RuwgEiejBnuMDQ66vybnBza+u10ZLM1bigpZKbuERVlPzWxRZiq3wKCIIilSP/RR/H45Vfg4LvePe/PDnMSBEEQBEEQBDFcpJnl7WD1r/wyACxaLIdYvpAIQwwVI8IoJ0xYJapYAo12wlSloGl0jwJIRBim3Cc8Ln9onUVPteemfRCUB4NjlgTrPZ7OYixz5vAg/V6TjqzCTTIQ+JeyNg3VfGrCGAfSaLLfq8Sv1AkjjBMGKO9LepwEOEteH+tOlra36zoAQDQHEWY+NL10X/QrxCBA14RpmPexP1otwihVxRZh+hXuLKGEu9BLx0xZPZPimjA+JF+YGf91TisZaxEmXXbwz/5saN9v7zc9PsvIn/d1451YmYQ9q57UHNKRRYcOY/tP/zSO/+OnF7JbBEEQc+LI334YAHDis5+d92eXWzqy2du+i23PvQhTX/23xe4KQRAEQRDEwqPjYq4DpidgUzoyYp6QCEMMlUilFNI1NSrFACv9kBFhKlI/cZ6IHI5I04vxioC6FMplIzkcZV3pV6QvC0xqtPRBuMw5Ex0+Yl5rEQayIh1ZzvXCMBcRJt0XdUHpaRW8XDeWBPmr0sDxjAij6uzI8nRgXBcgkwJCOWFYRb2c3hNPZd4HR05U9h2oFwxsLtwyal7PRtXrllGE2Gtnl1XMNtUOopbnwHOS8RZUBIOlSPbDdCsdM2X1TMqcMDJYoNmvNU4rLf7YIkzw6LahfX3PcrNocbaMvOuB6oGsHO7aeRz37D4+p7b9yY55zeeQwu7w/30fwqe249Bf/MUz7h9BEMSwYH7qvJ3vBAsZL68UjHv/638FAOz/X/9rkXtCEARBEARxEtDpyBwXYEmsSEqKWxDzg0QYYqhoJ0HLV7VJ5uDIAKx0ZBXB16J0ZKLCCRE7ScCbWU6YXmWu7uR0yIgwQYkTxtqu1AlTLcJIsIFlVcynJowOcq9VTpiqmjBRrIUUaWrrsCoRRqROmIZMvqfltEvbRycmM+9nDx2s7HseWSHEAUBguZ8OxQ9WryuKELutgWVl6PHqew6aXnI8q5wwug5Qr8nB1DiIS4LHMioWYeJ57p+5Uify6fEnFuhXwN5v0725p94DSIRZKcwGMX7x7+7A6z5yB4I5BBj7x2fM67mIMPzY3MQdgiCIkwHzLBfttsfm92Fef80jCIJonHPOYneBIAjilETquJjjJP8AoCZ2RRB5SIQhhop2srSVCFNVmyST0ks7W6ouYnEqwmjxQFQIGdqlMNW2nDOzR0rbM3U62E6bMieMtAKEjhJ4UJmOLAZ3G/aSeaUjqxMmItU2dSDJUneJrvHCJEe/oQWZ8kuBsESY9Uoscyoi9yK3rkf33l/ZdyBbY6euKHtgCW8Nfkb1esMQwk1nprZ6R2vSkSX7zHeYERL7FcFjXQcocjgcNSZ5ULx+GafpyDhL2krHB5+ZrdyGZ0rd+EKBE2aY2CJMXQCe0pGtTE500mv8VLdmPAKIwnQczEmEmZ2pbUMQBHGysAu1Yg4zI91168xr0V9e6cgIglgkKPUNQRDE4qAzxLgOmMqagorJzwRRBIkwxFDRoku7oWplVATUeTep2cIkN2mxZoLy9FJCBeUyTpiqdA/K3RH43LTvVxb8dlV/hBF5gpJAoE6DlvQ9LdBVKpbEEbjlyBDMQ3TgQEVfsoHpshRXmkh9b9NPT+kyQUunjIOUCLx6J4x24TAIuEztj4rdmK+Nc+/T91b2XX3KvKrb1sAS3sKa4L6MIggnFWGE41eKE3r8eq5jRJigYsxoJ0zkCzhS1VgpE2GsmjCB11H98SBmhyPC5FOf8MnJObU/GSJMWONsyTu9yAmzMuhY189H9k/Xto+tc38uIkz/gWonHEEQxEmF2SLMHAKl1oP7kQ98YAE6RBDESiPctWuxu0AQBHFKYlKPOS5mv3MbAODY3//9IvaIWI6QCEMMFR34bykxoKomDO+ogu1WgXhekY5BRsmsalfEJr2YqGqvRJXj42k6sjAoTkfGhTRukL3ruQmoB2Fx/7UgBCmMAyLpT0k9kCjKOGG418Kxj1VfsO1AdNivSeeUcyABqasjTxinAlKrPZ68rqjxkq5GwFEijKxqn0u75gqvpGWKHavIp6bKE/BU5AhFTd2TKALPiTC8ovitTkcmnS4aqtuV6ciYEhtdYcZYafA4jhErEaavRRjm1aalmyv59fQerAlQKxFGMKCjsqSt+tmfHUpfAKBnizA1zhYe5WvC0Cy/lYAtvs0E9eNczFOEIQiCWFIw6/5nLjMjrZuf/sMPL0CHCIIgCIIgiKGgJyc7DOHOnYvcGWK5QiIMMVTinBhQVSB+6oavAwCYlMYJw6rqqigHg+2EGQ3LawK4al2dZuqEMcLPQL8F9OlwYjStlRKW1IQROsAsBR4423KslIhOMo4hnFSMiN0W3NWrS/sOZAPTQac6IKnFrpYlwpQJYN/ffweAxPHjqvRlVenIJo4lgkErsJwwFe3ztXFc6Ze0TMm6fqoDF6GVjizgnYqW2gmT7nfh+hBheV2gSEg4rX24cfa30Vn1j8l3VPRHpyMLvXSMxSXBZhEExgnTt5wwsiYP/LW3XIv/euN/LU0vZ/qSc/iwVqukpUKJMLEL3PiCJHDkrJqo/sw8sF1ndY6l/DEnJ8zKwL4GTffq05EJ67jHJddqgiCIJYslwtT9Zs+1zXIg78QlCGJhWCnXDIIgiOVI9667khdO+YRkgqiDRBhiqHAhMB520Pz+3WgKUZmOrPd4UrSUSZGKMFW1T0Ll4LBqtmwM9pQ2d/tJ8H3MbwNahOn1CtsmYpGqY+MKU+elZ9WtseFhspxB4MDm9CJclo5MxjEks0QYfxSt511a2ncgG4iOKtwYgO1AspwwJQLY4U5SF4dJCb+7Vy0t/yEZm0pEi9GugMt0P8rdLWzV6sx7R9Y7YWz3S60II9Jg7vGRz2A6LE9zJMMQwkkdSMLxK4O7MRdon/EpAECvcQ+AcieMlNI4YbjthClLR9bvQ2gRxtcijG9mVBTxrT3fwk27b8KdB+7E9/Z/r7QdMCjCiNlqgQpqPwvHSkk2JFcOAPStekpVtaGAQfFSkAizIggtZ8vUHEQYbol1U9+8eUH6RBAEsWA4lhNmLsLECskjLiscxgRBDBESYQiCIBad2owjBFEBiTDEUImFxF/e+Rk8+oNRvPFEMhO6bNZO964kyM2QijCVD61x6oTRTpWD7umlzaVSqC94YgZCiQeiV+yCSASL5HTgLgeTWoQpFm2kXo/k4NbMx/J0ZFknTOiP1Qa8bTEiDqsf5rXg0vAcMxEzKnm4131kksPxdB+q1HxHtReI1LpllRPGzYouzhzSkdnbKmpSUYU8G8x96MhDpW1FmHXCAEA4XV7MO4gFHD8VdZz2bvTLBAEhjBMmZumYESWiDe/0TGq0wE3GlXA8TP3rv5b256/u+ivz+r7D95W2AwAZ5kSYmZqi5VZNGK4CRzIanghjO4iqxFhgUHQhJ8zKwHbCzM4hHZl93LfLC6vXvX9/9rN1oiNBEMQC03ruReZ1Vf05IJnIUfs7vYTxT0/vvwWJMARxclghwi1BEMRypq72LkFUQSIMMVS4kMBEEjxruKOQorxAvA5gM6smTNXNJdMijExrthxlxSIJAAjlUogbSR0ZAECv+EEx4jGg2l+87nxT56VfUkNGBImbgkGCO57pf6kIE0emPwAg3AZkTWA6k6KrpDaNJlb7zXcYfEcJAyV94aotg4TTVo4e5pp6KAN9N4VmBWaYTnFVLqzw3PfOJR1ZPA8nTFSTvivTlyDKFspFuRsKGEyZNHr2RxCUuZA4T0UYS7iLS2pZ8DDti+fOAlBOmAqu2nJV+nlRLcQNOGG65UFpKSWYGgfcSVKSAUD49N7Sz8wHKWVGeOFCll4HgDT1nusl+6fsPCKWF7YIE85BWLOPe+SPVbbt3nNP9rv27J5zvyidB0EQC8HMN79pXsuw2EmtCXfuWuDeLCyskbqMZcV9FUEQQ4REGIIgiMWHniWJZwGJMMRQiYVE5I+Y901ZXiDeBPetdGRVF7Q1wSEAgGOlIzvhzJa216miHn3RengqADi+bVdxv8MA+nRwGw0j8gQlThjRmVRfwiGYa5w5pTVh8rVJmFt7I50RYSqC0kJI6F3sOgyem7gaSuvxRLr+jkDcSgP73ZLUa7ZYJpjuU/mlY6qvZnaqYzr0dGTzEGHigtRgcYXTphPGEPFIZlmZE0ZybsbYRhHB1c6SEqEvtmr8nOkp0c6p3jebRzennxfV2z1787cy70W/vPaN7cISDLhsZ9K37h13Vn7HXCk658tqFAHpeeO3VHo3csKsCOYrwgR79pnXE1M7qhs7OXF1ujwtYeY7du7Eky/5URz7+Mfn1J4gCGKudO9Mf0PrnDComVix1LEL0oo+OWEI4mRQNImEJpYQBEGcZOi6SzwLSIQhhkrMBfrNteb9hGSlqYjYaDLT2a4JU3U9m9F1YyQ3ThVHlqfR4q5ywoynokprslhUiYKeccK09h0zokpYIsLwvl4uIJlb64RBHENaAXfpeOC16cjSdVU5A+yAt+c68FRqqbJ0ZFCpzZgU6LTSNp2Sh+jJNUkdk32bHQhHfRfzSm/6oyhZj8cTEaBOhJFCZrav3glTX1tCE/cH93Fcsd8jLuE5WfGizAkjuTAiDHNiM8amvnVLcV+00CRjeJ6ux1K9b4QUuPjgS/DDe34K/bhCVAEw/bUbsv0rEdWSvqfbxB3g0t3DvZEoEgCDiuOqj7nf1CIM3disBEJrHARxfcAxPHrMvHZkdXvmZq/9vfvvr11/76GHseM114AfP47D/+d9te0JgiCeKXVOGObXu4SXC7JPThiCOCkUPXvVCb4EQRDEcJESm97+NgCAt3HjIneGWG6QCEMMDe3I6I2kF6JxwRCVBF/bL/whAEmB+DQdWfn6DzCVOkty074qwK8D5BOtMSPaHDtjfWHbOLIels8/y3LClKQjMwFFAcG8VEQqc/3EMQTL9jWuCUradTJElYvAFmEcBt+tTke20dukOiXA/PTGvdMvDhgI5YQRjgSHJQyVbavqqhsnYoyD6kBDXnSxU5MVEeUcIbzCUWRSg8kQ0DVbKgQtLiSEk11fqXjAY+MS8hmHo/rV376zsLlx4MgQvpcIZfkxkSeKI7xs5y/iBfuuxq13/6CyrbtmTea9LBm7ACDjrAgzbIqE10onjNrHjVayP6SQpeOLWD7Y1/65OGF0HS8AmfSNRXibNmXeH3n/B2rXv+s//sfaNgRBEMOgzgnDvHqX8HLhxD//82J3gSBODQqeeWTNpD6CIAhiuEz8zGvhbdkCAPDPOGORe0MsN0iEIYYGV7NzIn/ULFslGKKy2iSqCDiDgMsTAcDh5YG3cd5O2ojYiCrrwtHCtnE/gC4232y0cHStUH0rfujlluPFee55xgmjXR15zA2vFJBsLjVh4gHXQ1kNFtOnyBZhygPStuPFc9N0ZGVBbxbqdXFcwtPtLhNhAKb+l6kTBoAocytIldZtjk6YvOjSn6mePZoXYcrGFwB0VV0UKSNAHaMq8aton/VLnDCCc+Nu6rnCjBlRcllVJY0gEeLIbDKudE2Y3kMPFX7mkW1pXZcGbxW20Yy94uWZ91G3W9pWWqIjd4F/fMVwfwrs/eir8VgVhNdjqdGygvCUkmzZY4txZY5IG1uUlDUizHIuaE0QxMpH1DhhVlImi8kvXLfYXSCIU4KiyX5lEwAJgiCI4WFngWlddBGYTo3Nl3d6WeLkQyIMMTS4kIAEIi+tqTHCRakYEASpkOFy5ZoQ5QH7Bk/+lnXCFA/h/u7dZla1zxwwlrQve+iNrVRcjZFR44SJS0QYEaVOGIi2SZ1TKsJEgyIMr3HC2A6Rqhtsu16M5zjw1A9CWS0eYWZnSjScVNDolRSU1yKMgMy4JsrcOVIdE08dUybn54SJwupgbb42SlVwv3/0sOrUNAA1BirazyeNlozSfrScVWbMyNZIYftYr0eGCNWx0WNi6stfKfzMrl3p+PNETeqS3Oy4+5++q7QpP3oUADDTAoTDsG1rcox9NaPj2aLPed9laChn1lycML4lwlTVQSKWB/YxD2ocbkBWeLFdMUUc/PM/f+YdIwiCWGBqa8LIFTTRYAWlViOIJU3BdaPK+U4QBEEMCUtsYY5j6pNSXS5ivpAIQwyNWEg0JQCWDqsrju4oDb5KtTzwBRyRPKwyXi7CMJEEih0Rw4uTWf5e3C7uS69vZlU3HAYdgIdghe15mAa7myOjqashKHYTaAFFQsARbdOel21rFEHmUk/xCtVccJERjPolYhCQOmEYA1yHGedBadDbEpA8V4IpUSOKikUYaTlhxpxZq49zc8L4rPgYaXguOFuVei1pkJ1dGlalI1MxEIHApCMrS9MGALEQ2JSz9Zc5YbglWp3L1pt0ZM5FFxWv2zQPIdQ+1U6aE5/5TOFnNvL0O/waJ4wMswGf49MHS9vqIrpBQ71Xhzjav38oNxJ6H/uug4aXjIcqsSxfEwYYHBfE8sM+5nNxwswnHVm0e88z7xhBEMRCUyvCZH9r3Q3F6XKXA+t+/dcXuwsEcWpQ8Mxz7BOfWISOEARBnFpI+/rruuSEIZ4xJMIQQ4NziVVx9qHz7JnDpemihIpKB54wzhaGYpEEAJhMgnKO5PCjJE2TJ4oD09HMtJlV3QBDS6XaWnO4OD0ED9NZRM3R1AnTOnSouL0JLgpwNNN0ZGWuiSAYTEdWUXw8X5j88OyR8rbKVeE5yb7zapwH0ogKHI6b1uMJwrKAgVL5IbGDnW5Em7Li6VK1d1UheYb5pSOrSr0GAM8Jd+F/foXjhx9XKeaqgvtqXZJxaCGuSuSJuESfZcdg2Qx+u65KyxvBodXJ+7Bkv5i+IAaHrrNTPXv0EnnCvPZ5o7JtftZtoyL+I9V5GqtfgJbVNj58uPJ75oIOuHsOMyJMaW0dpGPJ9R04SkSsFeOIJY997a86/pr5pCMjCIJYytSlI8sHU/mRoxD95TmjnZEThiBODgUTpaJ9+xehIwRBEKcY1n1b1glDMQtifpAIQwyNWAi88lC2toUvy8UAoYL+kklT48WRbulMfCYc9X9aBF0vyxMdP2FmVbsbVmPD0aT2CXeblX2BFPDbqbPFn+oVtu+YGjICnLXA1MU3iEqK2/d7A+l1ov3FAg8wmKKrbDuB1HWg05D5SoQpc3w43cQFIZlUIozqe2lhx1SEiTL1b8p+cJLt1OnIUFMTZtAJUy7CTHZDvOShw3jpIxJv+WLyuUqHhdJJJDiaSnzqf+2bpe1jIRBbIkzcORf9krRxs3em6b52eDOAPJ4s7xcLiVqgk4gRKUExL8wNdmiVedkSxWnONLpOUVdpNX6VmKXa6vRynt12CAU+9Tnf8BwzHueSjsx1HbhKtCkT+Yjlg33Mq85TjX2NrEtHlqd5/nPm1Z4gCGIhqUtHVpRmdvrfv75Q3VlQqDA4QZwcip6RZVzjuiMIgiCePbbjxRJhQHW5iHlCIgwxNGIh0c6NKOF4pcHXrnKzCJY6YYCK+icqzVV3FTNOFUcWB+piuyj52ZvhKEGgTISJQl2/hMNrjZj1i5K+7OkmNTUkOGI0EfljAIBer1iEkf1+ZpY3APRv/0FhW6BAhJHlDqE454Rp1KQjc7t6XwgwB6aeTVBTE0YyIHT82vo3jnK+MKGdMNXB1Py2Vokwh6YDjPey/ayaYS+0CMM4XJ1ibqa8YP268ABsyaXN+qXpyGa+d4d5PdochUSy3rK4i3Z+SXCESpiqc8IwPmFer5/ZWpkqTAd8jk9shGBejRNGiTDq0EyPpONL9IqFx/kQxc8sHZl0BByPZZYRy5dMTZg5OWHmno4sT/uKF8yrPUEQxLDxzzzTvJ78/Bcq24rZmYFlsmQiz1KHRBiCODkEjz02uLAknTRBEAQxPCgdGTEsSIQhhkYsJMCyKZMSEaYkcKyWJyJMelErC8LrdGTSS2vIOKLYORP10xoqDb+Fpy5Ngt3cbUIEg/VVhEpHxqSA37ZqwpSIH2MsEXMkE+hjBFFjHACw/4nJwvai1x9wPeRrxNjk3SFVRcpjrl0Eqrh6XToytVwyDuZKs61xieNDGicMMNsITfugJNWG7iqTej9XB1MH0pFVzCYIYo4xZEWC3cfKRRUutIDETRq1vBhm8yPhnRknzGnsaGnw2D/v/OSFFPi5zT8GxpLtLdFsIOK0Fk8Av7YvAODEqQhz9onnIZblD1oyirDzrJ/EE5f+Ce5+wVvQqHgmkzknzO5NcxNhIhHhRP9E6d/TdqrGi+ugYcZj+XEVSrT52sP/gqkgWT+lI1v+hBknTP0Nqp2CTPrFgnnpZzkFIQiCWFxGrrjcvI4PltdlA4AjH/rQ4MJlNJvSXbPGvKaZ+ARxcpj51s0Dy+pcdwRBEMQQsMSWJB2Zik1SOjJinpAIQwwNziWQCyoLVu6EYSZALjNOmLIgPNNigJcWk3ekVxgk57YI02yiP66Wu010vnf7QPsoSAL5TAp4jZZxwjSniwN7rk6NBoGe04KrnDYlRhuIXndAhBEV6XbyLgBZWUw+m46stgaHUUlEJh1ZWHoTr44TJNpOZI5VGBbnLhf6OEn99/mmIyv/IQtigQ1sMrPMqbiKidmkrWTc9LsqzRETIZ7/lMQbvs7hxRKjrFvqhPHOPiv5jOQYHVkPqUQYzos7JNQPt2QcsVQiTE06MjdalXkf8fIHLRlF2HnOawEAs+Nb4VeJMFFWhAGA6c3JSSK65SLML//bL+Nln3sZ9s7srey3rtPjuWlNmLBiloge7y9/MMLq2UTc49HyCUYRxURWSrlwDqKa9FIRX8739mQOIg9BEMRCUuVWzRPtKfgdXU4P8va2khOGIE4ObHByIDnRCIIgFp7MPZ7rgqksNKCJo8Q8IRGGGBqxEHBZTjxwvNLgm0475oJnRZgywUGnHvOkSYnlSLcwSM4tl4bv+3BVkinuNsFPDM7kjwLthOForFpjatSsPlwc9NZ9ZFKgy5rYePhe1beSrhc4YQTzIDqdwvZ5EabltIpXDLsmjEpHVpP+6Wg3efCPmUycMGpfejv3lHyDKiIPYPPsqKnHEwZlwXqdjiwRJUaC4dWECaLBbapMczWTpPsIXZ6mUatKcyQ53nadwNX3SfzkPRKCAf2C7wSAyIhWAq3R9ZBOMoZ4Sf2eWI1TCQ5HOaxqRRjLCRO4XUSiWoSxqXbCJG1jB5hQ4kjUVCnSZqZLP/fEiScAAN/a/a3KfmvXS8NywswlHZkjYiOwcrqhWfZk0pGVnEc29rk5XxFGPgMr+DP5DEEQRCnzmDtQNHNSLqffPSsYIUkEJ4hFg0QYgiCIk4DthGEMcNVzq1hG927EkoBEGGJocCEx0coG8oXjlRaI144MOYd0ZFLKNB2ZDyMEOMIrDJLzvhJhJEfDbcBT4hB3G5j60pcG2oswERSME0YJCJE/Wth149ZgAl2nBaacM9H+Q8XtwwCSZet/SMcrDQIOuABKAvtAIn4BiesAsNORFe93odL2hEyAuamgNfrgI4XtmarFI1wHo50wdcKUpq1KjtNEt6+6Xl33JJ8GrUqEiYSABDC56jw8ctGvoxWNIqoIpEqnnbxwOKZGlAiyfn1p+xOybV6vm5YQkKWOoihMx9jI2DpIphwcJXWKuDpOAhxrMZu8rtg3nAv4fMy8F0xUizBhaGofAYAfl9cR0jcR3AXaKpAyu24k2a79B8o/p2AFM/FsdPDddx00/RpnFmxRk5tzm2rCLH9sEWZOThiWXucEq7498bdsyX72GaQjo8AFQRBDZR5OmKLUY8sprZfde7qWEsQiQhNKCIIgFhwzUUaLLyoeIkmEIeYJiTDE0IiFxKQqqm1qqjjl6cj0w6pkAvGLz0xrdhS0TwJ4yQWPWSKMK8qcMPqBkMN3fDhe8l3cbaL7gx8Mtg+SALojOVy/gVb/ePK9jYmBtgAgdSFxCHRYy/QnemJn8bZGcYETxi19YB8IQFdc29N0ZHknTPFNuU6lJpkEc2AEMFHSF6bSkXHHwZdP+1Ej2kThYG0dKSWYms3uKUFAOF7lj9OAE6YiJ3rMJU57wsO9l78Zhzb9EF7+1H9GzMsFgZ6bpDcKmEC/ocbkqvHS9kxm69wIBgQl6cgik46NY2RsDQ4r60ksivujxSbJOG7llyevHQ8Sxe2DTjao4YlGZToyHkaZILYry2tqpDVhGFrquEdNJbYFxWnmbAsuK+mzJhVhGJpeMh6qnBD6mDNpOZbieQSziCVJtiZM9Q2qlBLCqh8lWXG9L82qn/vZ7IKamdhFgrekYrYEQQyT3DUrfHrfnNsCWFY1Yez+T33xi4vYEYI4dfA3bRxYRgFAgiCIk4CKbzKVC1//T04YYr6QCEMMDS4kZlrJzH6PJy6JqpowRlhgEs55pxsxIC4I1gWxAFMBOseFEWxaoYd+QfBNqNRMUokwk+cmYgpXRVvywT0eaVeDgOO6cJXjgJc4FXQqNQmZEWF4ySkl40ERptIJo/ZB5Ciho+LaznVaNy3C6PRPJfvdpMJiHIwBjki2XZSIB9BOGM/DNvcsI7BFwaAIYwsobqycMMyDLGhr+p8Ltlc5YbgQ8GIGKLFhTW9T+fgCMKPGlGQckikRpCJW6yDtJwMQwSl1cASm7pDASHscoatEJ1Y8ZmLj/OKYwYhZXlYbqB9mA8S+aCCIy/djEESQ1nh1ZKs0iK2Dz7EDNNUxi5SAKvvF37Ht+Dbzut4Jo4RB10HT1Cgq3/Ei0ukFRZqOjJwwyx7bjVflhALUNTU3rqoEWQ1TDkDRLxYPNXvf+N8HvzMKC1oSBEE8Q3K/ubLXLW9b9NAultGM9vm4fgiCGAre5s3JC9d6dqAAIEEQxIIz4IRRMZyi9LIEUQWJMMTQiLgAU/VA3DgRYSLPK50BrZ/fJBNoNNomzVUQDj6EJutQThiPwZE6QF2cjiwO1N9lDN/14bZ1IXQfgjnoP/hgpj0PE9FFC0EOV8KE20ARwipu33FSEUaUOARkHBU6YcpSOBgRxlUpriqFCe2ESU7nRk06MkeLKkyJN8qxMrNqXWF7pi4ToTsGwRzLCTMYwLTdCwxpOjJZ0Nb0f6AmTPkPWX6bYieqLPgO5coRjEMydYwqArv724cBJGk2GNuAvvQLnVYAEPZU0FcKtBoNnKXSiEhnrLC9ThsnwRFYxYPKUpIFvUExpF8hZnXzMWinAUTFzhmd8oS7ME6Y7b2kVpAsccI8eORBtPsSFzwtIWoCRVoYc90Anpvs98p0ZPr8E9yc2yTCLH8i6xhyIc21qogi8bVKkNUzP/X/ndtuq+xL0d/zdZQIgiCeDfkHcVlxzZMFBWSq2i85KPBLECcf9cwz+qIXmUXkhCEIgjgJqHs844BRE6CxnOr5EUsCEmGIocGFhCuTgLKrnDAOvIGaHwblyJBMwvcaJrjfDwbbB7YIY6Ujk8xD1Dkx0F6Y7xTwHR/NVhroFk4D+9/29uwHVARb90E7YcoC5FookBAInIapCVMWM5S9WQiWE2EcrzSP76ATptx5oNOROeqHwFeOhrKgt2PSkak6OSz5jn5jpLC9dsIwrwEJZsSyOCpwwlg/QjMXTyXLakSYMM7+bbY/W9qWi2zYwuUxHj0wVdpejxkBDqG2t+pZZdfIMQDAgdOuQnP0T3DZ3p8vF2HMLHoOz3PhOamgNnN8UMiYNs4gnjE2SeaBtVoD7YNOsh+8KJ1JG8yWz6rt53YxdxsQJfs9TUcGIxuGaqiLoPgzDnPw7k9z/NmnOVbd9lBpPwDlwmIBHvb/J24J35D0r2Q/AlkR1AiaFQF4YnmQd6lVudaKRDdeMWZM2h5Z7cqqhOoYEAQxTPI/W1WzIwt+4rwNG4banQWFnDAEcdIxaVQ9ywlDNWEIgiAWHn2t1enItCOGhHBinpAIQwyNWEj4cZLuqxF2ACQB+NF77ihsb55NtRNGza7vBoOzk8NYGFcDazgmUAvHR9DrDLQXUeo68B0frdYIBHQx8ibCnTvBZ2ZM+/b9Sd5u44Qx6ciKnTA6ii6ZROT4aToyv7i9jAadMHNKR6ZSXDlwSmdIinxNGPWDUBbwdI0TRgCbLweQBN396cH9CKROGPgeBGMmXVQUDh4nO5DquiolneNVijDx8acz7x88XB7gj7iAsI6JK120/PIgbEOlBmvBMU6YKlcR1BjZfm5Sb+I5R16KXsQL03rpmjhMfUY66f7Yfu/hgfZMJsdfggMM4CxNeSf7/YHviDrJ8XBEZJxZ/e4MygijXFo3td5CLBHmgVZyzoYmHVl5TZgzjyav19+2rbCNWT2XcEd248zDEufvk2isv6nSCaNrODEpzHUgrgrAE8uCfErEahGmwAlT8Fugmbnppnn1hTUGr83khCEIYqjk05FVBEfHfvRHzevmxRcBAFijeOLPUiQzIWb16sXqBkGcUkiu7pe99Fohl1MaQ4IgiGWKcR1q8UWlxyc3IjFfSIQhhgYXEucebgMAGlESLBaOB+9gcWFSXdxbMgHfbxmHRaegJkVST0LVhPFc4zwBgKBfIMLE2imTiDCjzQkjaMSqLszRD33ItHd6aRAYAFyTjqzECaNFGEhErpfO3neKTykRRZAD6cgqnDAqoB676b4oS6OVd8I0VA2OsjRwOh2ZwzgwugGjatu3PPpkYXt9mXAaHiRjxi3ECwKYPLDSSvWSfgnHK3VkAEDczTpZimoCmfULidhrm/dj0RbwisCuriM0Jl3jhGFTxSKDEBLQbazZq0ICs8HgjPlY12xRbcd66bEcWzPobPEDVRPGU44cR6cvS8bF7Le+lWkfKnHRkTFcVbcn2PtA8YYCiHJBbO42SmvxmJow1kQ6bSiYvvHG4vWL9Hj7JQ4x05YLNDd+Be/7OMef/yPHpvY3q9ORqf5IWOnIKlw/xPIgfw0qS5EIpAKuI6LUbdercNDt3DmwrKwGEgCse+N/G2xPIgxBEMMkfw2qeDD3Tz8dADDx2tfCnVilPr9QHRs+spv+RleJTQRBDA/tZGd2TRhKhUMQBLHwqHsdnY6M6XRkJMIQ84REGGJoxEKiGSfBWT9KUikJx0d/bPVAWyllJh2Z57dMcL9IhAljAanSeTlNywkDoNMdTF8ldRF0JDVhRlsTpr6KUCJMuGdvuv6RZJa0Dv6lTpjiYLN+zhZMgjHg6GrtsihsDmE5YXSKMeG49U4YxxJhSgKYXF34UydM8n+ZCOPK5MZ9vWDAUzdVbquU0qj8rudDgBlXRtAf7HuaVoqjv0EdI+YgKqjzYz6jtosrp4oWiYqIhMSBDdmaK6JgBr3pvxJhJJNw1ZiJvUGBJFm3MCJM2FyV+VunIEVeKkIlf1szHWGNKl5vBBoLR4kwUVOi5TsQLPmcHhfhrl2Z9n01rh0Rm30espKUcQDE00cz70+sPh+9Rx8tbGunI7tAOU7aqn9iqji9W+vex81rv0Sc1ERcYF18xLzfckwiqExHlvTn+Dg353bcX7kB8t6DDyLcvXuxu7HgzCcdWbDvIACAidi4oXjBb0ElFYFA5noDyya/cN381k8QBFFFXoSpEIb1jHZndCTNC7pMUnwF27dnF5AIQxAnB5Vum3npPQ05YQiCIBaeASeM+p+cMMR8IRGGGBpcCFP3xNP1LxzPuFKyjbkJ7oNJuF7bBF+7JSJMWhPGwcjFF5q/9buDM+Z1LQHJkpowE63VRtDQThhndNS07542rrqSXERPnLc6aeuVpSNTAhIExpseAF30vaR5GEGodGqhnaarpCaBFmHOOVDvhNFxTTfnhClPR5YcoyayqdcK698IAcn0en1IxtBQAlvYKajhEGhHEUdvTbptk7PThX0BzPMEYkeJZBWFaWMuMLm+mVkmq0QYNcYEOGZHkn0ZNiYKxa+YSwDCjA8Ng1BOrFx7q+4QADhrp00tobg7OIalutxKR2Ck4UGw7H6XueM120vEECZi48wKg4p9kxOXdp/1Ghz92w8XtpWxFt6A/xUk3//omclxdiYmCj+z6ev3mtePHisWdzQhF9h6JKnfI1jioIqqjqtywkSuSNK1IRVmVhrRvn3Y9Yu/hO2v/snF7sqCk3e+lAnDANB57AkAieioxXDeqxdhvHZ6blYKWwW1GY5/6lO16ycIgpg7ye9epB27VQ/mZkalC6bvh6tqyCwheG6yBjlhCOLkYJ4b7ZowZbVXCYIgiOGh7umYio1B/19xDySlRO/++zNlEAiCRBhiaMRcQjrJTaHHtQjjA71BkUTGsQmQSybB/IapNdIrKAwexAJSiRiO76F59tnGHdALCkQYnq0JMzayBrFywnAVZHfXrjHttcuk21ROiNNGVd/8whQ3Urt4IDHe8rF2SqVT6hTP3hdxBKmC7aHXN+vOux9M/1Wwct10ui9EiagSqx8EV/0Q+G51OjKm+s4clcasSoTh3Ox33/chwcxxO7ZzdKC5UE4YR3JI5sDhieB0wxPFKa7sfmoRRlalLBISLsv2U9f/KcI4YSDge4kQxJ1mofgVc4kLOy5CfzyzvImwMJWWMA89yf+9cyKzL6MaEabtuxBqe43ok5vJ1uvY6ciS9e2dPVa6rdzNijAbDt+L9qteUdg2CJJ1c+807D34i7isy9BpJuPCXbWq8DNuN03jtnd9aTcAJPtyJAAeufi38N0X/wW2nBhFVJmOTO03V8BTs4N5RT2Q5UywYzCN1kplPk4Yoa5LjojTlIcVThhnJHGFbbwsFXin//3r5Z0pmmFup/MgCIJ4lkgp8dR5P4/vvvg9OL76gsoUiVLfQ3hu+iC/TJwwpr8KEmEI4tkz3Y8qJ6IBZTVhlod4SxAEsZwxE2bV86M7lmRnEZ1O6cTqmW/ciF2//CvY+bpfOCl9JJYHy06E+fCHP4xzzjkHrVYLV155JW677bbK9rfeeiuuvPJKtFotnHvuufjoRz96knp66sGFNAF7N05FGNkbrMEh49gKSkvAa5paEL2woCZMyAG97qYL5rppnYyCQuL6JlawGL7jY9XI2jQNmJu4W7z1GzJ9BxLnDAA0deok5hamAdMijGDAeMtDu6/SSkwV1zDo9wOTdkoy5fhwPBz4oz8ubC9UwNoVoZkZWZaOTKiHds/NOmGCMieMcvEwlnxuaiTpe+APpuvhMYdUeTL8pgcwhgObX5y0n24OttcF1kWM893DcJUY1+uUFIgHEMe6/o0Sgyqe5SMu4U1l05E14vJgvR6PUko4Tlrnp6gWRCQEVoUCsZ+m/JIQaMgA/YJUWmktmuR/n0lAKjGhoJaFdkJJJjHScBG7yT7hasaszM1k6/USAStJR5as954Te0q3Ne+EcUSMR0VxPaYw7EMCaIz9bxw+chUu2P1fEKnDH+3dW/gZdzqdwXHegdJuJOvgAr/4HYbDG1+A2B/Fa++71NQuKkILWqGXpiMLDh6u/hJiyRMOiDAVY0DXqpJpOrK4X14TxjttY9K+kX6HrLoWFAQp1v7ar5W2JwiCmC/R7t3Yu/WVkI6P+y//vUpRRcwmv6nMcQFHF3ddHiIMy9c/JBGGIJ4V7/36Y3j+n96In/6b71YLMUU1YUiEIQiCWHhEtiaMu3p1slxKiM5gjWoAmP56MkEw2lMewyFOPZaVCPO5z30Ob37zm/GOd7wD9913H1760pfiNa95DfaUDOqdO3fimmuuwUtf+lLcd999+MM//EP87u/+Lq6//vqT3PNTg1hIk45MB98BgBXMaJdRZNJcJU6Ylgm89Qva962aIm6zAXhuWpukwDkjlYghwOE5HlaPrcWWmecAAI6svyxZjzXjX5rVJ59b/8BT6XcXuFsYT/oeOxITLb/aTQJgaqZn9g13rVRtJfU3eKxdKums8DIRJlbLdToy7YQpcx60AjXjnElg649g29bkhn5mZLDvYRQZ50u7OSi6DPRbizBSwJfpOOBB+QOC7r8WyaqCEFwITPTbmWWjvLyAuxZhODigRCfhFIswXEg44Ii8VIRhcDDC+sVOmJwIs3/kMkiUpyPT6fSkk4gw3En2TawcLEKJLpp+kPw9SUeWrG8tsgKU2U4pIZxEXOy7qh6T6+O2Xd8ubI+Yo9dK7SyjvdMRWoe/98ADg70P0hkez9tdHSgKucCa/kbzngle6YLgSoQJfAGmxFjtjChDSom/vOsv8ZlHP1PZbiVwZCYwQvFy4pk6YWCcMOUijM7DaE/ILjqvrS8YWKTdNARBEMMgePKpzPt4crK07YnP/jMAYOorX0kXLhcnjJNzEUpJs/EJ4llwy+NJHcVHD0xjqlcxoUTXhPGpJgxBEMTJxLh+tQhu1+Yqc8J8vSJLA3HKsqxEmL/+67/Gb/3Wb+ENb3gDLrroIrz//e/H1q1b8ZGPfKSw/Uc/+lGceeaZeP/734+LLroIb3jDG/Cbv/mbeN/73neSe35qwIWVjiy2nA9hgUhiBffhAGivNk6YuD870D62XAJO00f3+3eZOhlROHiz6u1L0jZxV4AxhomRteZvxzdcMth5E/BVRe6tWX0PfvvpgeZOlLQPPYbxlmfSr82Mnzm4bgCuFMYJw53UCVMGVym2mIxNnZoyEUYHZ12WdcLkZ6FrzH73Aaw5G7Gj99+gCBOEgWnfbCZB/uc+9snSfosorQnjS2nSke08NrgPNaYmjEoX5xX0QxNxadLJaVwhS1N+6HRksZTosaT/wvEhC8ZMxAWYEIi9bGC21dqPoCDlGTNul2QDpkfOg1Aup6Ki8nb6vXbDBXd1jZok/dnxT3wi0z6eUunIRGxcXy1RIoRFkRlPoZeIUtxtgpXkiZZRhJmJs8x77s0istNLHz068JkDl67JvI9E+UNiFEvMjp5urb+FqCJntb5xCT2Oo+NJu+jYZGl7AHjo6EP4p23/hL+66y8r2y05WLW4lOeuncfxw3/xTbz5c/fPqX0ccXzhPT/ANz/5aGUqnJNBlKvXVHZNAtKJ1ExEYLouUIHAblABvzdsSR2NKLkBBmAchat+9mfMoqMf+lB5e4IgiGfJ03/23to2/PhxfP/gXcmbZVITBk7B71jV9ZcgiEo6Vh3EqnslE+hzrWfIivYEQRDEkFDP1doJwxgzgkw+owlBVFEeBV5ihGGIe+65B29729syy6+++mrcfvvthZ+54447cPXVV2eWvfrVr8bHP/5xRFEE3y8IOgcBgiCdxT49neSbj6IIUdUsWwJBFKVOGBFCSgHGHDznzm8iDP8kLWIFIOr3UzGASXDHM2mI4u7kwL7u27OCPA/R00/D3ZQcpzgcPDZc5cqNXY4oiuBIF/dt+Qau2P9qrFEpmngcm8+Z514mkmWWzbs54g4e+zjZln5DYrThwotTF0Ov04fXyM4SHHFkKsKo4Lt+XzSuQl1bRajUPC4QBiH8SA58JlQ35A5LlrtI2gQRLxmzyoHkCkQ/8juI7/hbAMBY4A+0n+3MmuPUaHgAQox1DgEA3FZvoH2oHSAyhg8JR4lTq9ja0vMnNjVhkr+v9srbhnE8IMI0hUSnF6DpD9Z3MMIHBCKZ/F04PqJeF8iPsSCCA55JRwYAbPwpzPaDgT6t3jmJAxcBE7PJfvadBiSS7e/OhAPttSsndhyMNVww1Z/t5/0HnLX3mwCyx9U5nKQqcWSajiyOWOG+Eb2eGU+Bm4xF7jRwxbHxwvZxbwYzY6lg6EcTiG0RJooHPhex7EPe3sm92Dq+1bSz20dBH712GhyP3SZGjh4oPa6CS4ABgSexaTLGrtVA99HHKq+5r//qf8Ln35vc8HR+9Ck0zjqrtO1SgvP0QXsuvyn/dOcuSAl89YH9+OtfuLS2/aGd0zi8ewaHd8/gh3/mLLTHG8+qv8+GMJeiphcMnheaKIwBeBknTNTtV4wZVTvITX9XeMX6g/1JDj3pZ/dH3THoP/IIJj/1Kaz7vd+Df/rpheOdIBYLGo8LTxAL7DnexXM2jGbuY+dCePTEnI5NIFXNwnjwt3cpwgtcL2EQwJnn/llo6PwglhJV43HGmrw12wuwtl1cs46rlN3SEkLlMrluEEQVdL0mlhJF4zFWMWLJrHiMetYNDh0E1mYnrJatk1iZzOf4LhsR5ujRo+CcY9OmTZnlmzZtwsGDBws/c/DgwcL2cRzj6NGj2Lx588Bn3vOe9+Cd73znwPIbb7wRI5S6pJL7DzOcr5wwjojBZASwJoTj49YPfhDd8883bf1jx6CNWEJw3H3PPWAiEU6OHTmEG264IbPuB/Y52IJRMMmx8+mnsR4wgemZqRl8/brrIJpNI560VcA79LhZV9A8DgDoKyHj4YcfxtRYUlw+1rOIpMANN9yArYJjw5H7cGTDFdj26MPY28umZ2KqfkGn0cHxQ/vw8HOPm7/d8NUb4bWzM8C3hiGkFmGYEmFUgfn8tgLA8R0NAE21H5OL+623fAf+eNLPm266ybR98CAD4OLwoYO44YYb8PCx5P3BI8cK161r6wgR4+bb70HkqvRPjo9vfP7z4GNpyqvJyeMAS97v3/c0gI0mNRYPBvsePHEcwFlgkuPL4sexTqiUWz1Z3BcA0zNdAOuMCBP2o9K2D++ZwcVuNojqA/jqv38DIwVXM+2EEQyIlfgkHA+3fPObiDZsyLQ92AUcyRF5o5nlI8EErvv2vQh2ZIMOrUbyhd1Wsm0zUzN4/nGO4xuBQzsOZLdBCEglUMZSYPrYIayfTgPqEgwM2X00q4LMiRNGuWZ6ceG+cbpdI4AGygkjXB/n332wsP3qfU+j37rKvG+EmxBZwex777kbs0G2jk8wm3WofefW72CNk95s2GPyxINHEXtp2jjutuA++ShuuKHYmRFyAXhAv5HWhOGOXzoOAOCf35sG+H/wNx/E1CteWdp2KTHyxBM4Q72u2j7Ngf0O9LVyLu17Bz0Ayb6/8YabzTVjMej1XQAMPpOIJMN3b/8+jj5a4uh76gCAC5QjMjm22x56BNsaxSkbz+124AHglp93944d+EHJPrrgS18CAExfd11med0+veAPkokfRx55FHt/501muT3eCWKxofG4cPztow6emHLwmxdwXLau2l14Qe69cLzSa8y5Y2PwZmfxzz/m4Px9yXoffPBBTLdahe2XEo0DB3B2btk3/v3fIeeQtnYxoPODWEoUjcfZXnK/BADfvPkWnFYSctjw5FNYA2DH7t3QOR6iMJzT/SFBLAfoek0sJezx2N6+HVsBzHa75pqr7/sefee7cOD1vzrwefu+kK7TK5tut7xEQp5lI8Jo8rPQpJSVM9OK2hct17z97W/Htddea95PT09j69atuPrqqzExMfFMu31KMP2DpxF87x4AwORrXwC2J4JEE8LxcMXpp2PVNdeYtuGOnbjl89sAAK7n4oU/9ELc9P3ks6Mjo7jGagsAJ769E50HnwYTHM+99DIAt5oUTePCxXPe+S74556Ls778JQDAt//m3wEA0uFmXV94/IvJyvzkAffSSy8xffrcF78FAGAOcM011+CJv/4ruCql2nPOOBMvuOaiTH/+7R/uBQC0ojFceuF52Hu4hdMOzSBqjONHX/wyrN2cDeQ/8u53msLsgZ86YfytWwe2FQC+fXwbntx7FExyI8K85CU/ilWbmrjpppvwqle9yji5Dt+xG9j5OM7YsgXXXPN8tB4/gv/3xH0Ym1iFa6550cC6v/CZZL/7votX/MTVuOnOpG4Idzz8+GWXo3lh+nPx5BOP4vZbkhznz7vkEuDQESPCMOHjmmuyTrNvd/4NR7YDAMfdrRfjNSK5GI2IDYXbCQD/dP+X0ZkEuBJhGhClbT99/Z8MOGF8KfGyl78SG8cHH/7/5XP/kGwbk4jVOc8dHy978QvRtERBAHjs4Ay+/qQcSEe2Om7gkueej2tefl5m+Vf+KXGv9EaSMdP/7FfNmGm4rcw2yCjCdf/ySQCA47u44NyzgCfTlF+x14YfdzOf+dRt9yMOk3oqOvWe67QK90187Bi+et0/AQD6vkpHpmrEFLV/8qYv42mevZ6d1n0BgCQlykVg2JD73L/9azbt149edBHOOu8FiKJoYEzes/9biL1UHA+aq7Cq3Sw9rp/66peSdj5Ht53Ukjm4+Sr89jUvLWwvpcT2P0hdkVt5jJeUrHup0V29Bvs/nqSee82rXgVW4Mi0+e6XHsFdRxL3Xtn+s7nvxr04dt8uAMCLfvjF2HTO4v1u/cHd38TzugwXSQ9fbgS44soX4uUXbihs+8js93HgthCOiMFZIsSdd9Y5uPCaVxW23/Xnf4QIwBkzr8CxNU9j3Ylt2LplC15Yso+e+oO3FS6v26f6c6MzM7jmmmsKxztBLBY0Hhee37vjRgDA53c38fZffUVl2ydz1xnJvNJrzNNfuA79u+/GwTXAc/Yny553yaWZe+WlSvDkk9j7/g9kll39ilfCXbW0npPo/CCWEmXjUQiJ37sjDfT9yIt/FJdsKT6Xjtz/AKYAnHfBhThxy60AAM9x5nR/SBBLGbpeE4vFkZkAjx2cwUvOWwdHuQyLxmP3jjux/2N/j/FVq8w1Vz8nbhwfxxUF12H7+fNVV1wBv8AEQKwMdAatubBsRJj169fDdd0B18vhw4cH3C6a0047rbC953lYt25d4WeazSaaBTO5fN+nH4Qa3M6McXs0J1aByRgSicPCkTKz/zis2iQM8PxGOgOe84F9LYUq2ixjjI6uTr5PiQGrDie1M6IdO+AEIdyxUUjo+hvpuoSuxaKGves45m9SzT5iLOmn4zpG5InDeLA/uu+igVUjTRzw03RqTDoD7ZmQicID4GhjGucDkI6HVa99beG40kUWmRSmJozDXNPWHo9Mrdf3ku8dVbVbwliWjNmkvcsA33UxOR4DHWByzYXJMuszbHKX2ZdjyvmhBQHJPLiOC8dNp6J7KsUWJMfZG9fBjQ8nfYtWlZ4/umxFzBIRhvGyfidzxKKcSNIQErxgnycoR5TjIlL1foTjw5UF38FcdJhE5Lczi8/FUYQCg8dUuaGkI+D7PsZFD4+docaY38w9YAmTjkw6wFirgR+c/1780JNvTbbdbcGPu5nP6BR5jozMPgf3ireTsTQdmafSkSmxqrA9jxH5Y5lFPk9n3059+tPY8o4/zPx97OAx6P0JAIjCzLoz18iYZ5ww/dY6IJ6uuIYqUdzlOLg5deiUtedWykgACL/57WVzfXa9dB8e+6v3YvM7/7S6vWOdX55Xmw4n7KQOIRGX78OTQcQlfrKXjMMflh4Eys5TQKrzKfRiSJX6TsaivP9xiMMbrsSFJ34OD1wGvOKW3wEr+O2oo6p97+FH0v51OuXjnSAWGRqPC89sECMQDGPN8kcn4fi59275/Yy6+REMuPDp5HX/rruw/j/9ypB6vHCIgm3yXAfeEh2DdH4QS4n8eOyG2XpKvOJeST8Tug0rK4CouFciiGUGXa+Jk83//NwPcM/uE/jAL1+On7389Mzf7PGo6y8zbzAe47jl93ua/b/xm3jOt745xJ4TS4n5XLec+iZLg0ajgSuvvHLAonjTTTfhxS9+ceFnrrrqqoH2N954I174whfSxX0BkP2+cXusaY1DIAmqC8cfKNjJpyYh9YWMAdH+/WBSiTAFha0iVSOFCY5GOylkbupkWAXuj37kw5BSmoA3Y2luPi3CcJUGLFO0Wqb1aQAAjVEj8gTdbMA3+ZxKpQZgou0hbPhwtGiTa59ZPwDH0em/PET79w+2BSBitb1SwlFOGMGL02DEQqr1Jvuz5SffFZQWCFPFxBwAY5vwE1EqOh7/XDZVTyBTwcmdSIzvRhAAEIW5VEczx5J1g8NreDjtyJMAgHZUniNTbxdX+8XeV4ONJfqttZlFnpSF2yqFMOvqeQ5CfcwcHzIcLPgdCYFGd9AJc5rsoxcWrF+q2joqYNx0XfQ9VaeI5wLlcWzODek6GGm4kN4sel6S4ou7TXi5mRE65bojYriqtg7C4hzRMopNOrIJtRvzwaBs+8g4ZTRMVgf3z9iV/e64M1vSEpBRiMgSYSJ/DIjL82TqADx8jkfWf76yHwAQdmYGlol+v6Dl0mbyc5+bV/sgrk8tFkXpWI3z5+cQ+NYdT+Ojf3En9uycrGzHhcxcs1qSIaooHsv1tlnpyEQuMGEj3Vam7pAESxXdISCCALt+4RfS9RdcMwiCWPnoeyoAODxd/TuTd+rq3+Xixuo65wATqqzgzNe//sw6eZKRBdfa7l0/WISeEMTyp5t7xgir7vXUdYNZE3rAy573nhnTN9yAvW/6HfCZwXttgiCIlcY9u08AAL724IHqhkoEL5oQ2b788trvifbtm3ffiJXJshFhAODaa6/FP/zDP+ATn/gEtm3bht///d/Hnj178MY3vhFAkkrs9a9/vWn/xje+Ebt378a1116Lbdu24ROf+AQ+/vGP4y1vectibcKKRghuxI9Gs4mZkVRskLlCRYf/6r0muM8YwCcn4Sj3hz87KGKISLtMOPzWGEZ+6IdMnYzYSW9Ewx07Vf0NFfBm1qxwle4qhgpOWw+RvlCih2rffOObjQjTm8zdhAph+s4Zw3jLB3cc44SJegWF0y1h4ex4tdkvU6pOQR7enUz6A2HSkcmCQqhAEuwEAE+JME11Y96Pym7iLRHGccBH0+D/8euz/YnCvtnWZjtxTjCZ1qmJg+yNPzNBU46GxzDST/Zdu1ee41wILcLo/VYsNCTfLcG9ZF1unEQtPOkUbquYnYVUY6PneQiZLcIMHiMuJP7Dv7oDNWGEGEE/KnjAMcKdEmE8D6s6qnbLTDZQI4UwfYHLMNJwcTHbi8hV9XXcJrz167P9UaKII2J4cZJizI2Ki6zLyK45pMRGt7gtAMg4MsGi085NUh60o7HS9pASsdvEXVe+Dfdd9ruQAOLpo6XNN+16LOOEifxROEF5nkzttnJ8gQNjiXCnt7mI2ROHBpbxycny/i9j7Pu8oPScTuGW8MLnINoAQHc6RFQgNObpRxx3fvox8D1dfOUDD1S2DWOBc2JLfOZBjQijXsgYQokwvEKEAZgR8gEg9kbQfM55Fe2LCbZvL1wuKPhAEASAc9env43T/aprEgYmN0inXISR6mFeLK1a9nND3T67VlaBacp1ThDPiPxEr/JJdMmkKyCZiW1wy5+bngn7rv3/MHvzzTj2sY8Ndb0EQRBLmbwgnkdqwdu65noqI5MzOlr0EYIoZFmJML/0S7+E97///XjXu96Fyy+/HN/5zndwww034KyzzgIAHDhwAHv27DHtzznnHNxwww245ZZbcPnll+Pd7343PvjBD+J1r3vdYm3CikbwGEIFmn3Ph6OdJ07D3DRq+o88glQMYGief74RMc58/NjgutUsZEfE8NrjWPOf/xNc5YSRYqNpFx08CBnHRoSBk15MuatrZYyoWdPp+qVKUeapGiatH/9xI8J078gFG3kqNgnGMN7ywJkHVwk5cTAY4NfrF+AQOpNZxQxJceBh9UEBpsQpXuKE0SKMTlvUrHPCaPFLpRFrtHrpunKzOOM4SkWYhnIXAXCUGybKiTA6bZyUHBsbq007iPrZoLEWYWT5wwQXEUIvCXJ4WoQB0C/YVjEzY/Zx6DoIVcor4foQweBs1ki5tWI/ccK0+sk47PLV6FWIMNJRKUXOeAmetzNZb34/gnPTF+k6aDdchIwhcpQI4zUHZvGfUIKgI2L4alv9uKRSp+W00ftRuE0c31BcJFfGsRFpxtclYkkrLhdhJI8wNXEOZse34sSaCzE9cQ749InS9gi6GUdR5I3g5793ffn6dYq8BtBpqX3iNCFKhMfp6744sGz2ttvK+7OEqEsnlkef30DxOM8TWzeQcxFhvn3fAfy/t34X//THd9S2PdENsVpdwFi/ui8z/QjjVnTxsqM7KkUYof7GZAypasKIqMIJE/PMebbz7J8c+J2ZC0c/+nfF66/oK0EQpw72dSsouhdQSCkHRBf9u1wIX8YijL6Btn/PnOFuSPDUU8vmd50gng35e6MqJ0waBPTgqFq1oz/6kgXpV3yi4j6fIAhihVH1nAogjdVY9zvxoWRi6OH3vnehukWsQJaVCAMAb3rTm7Br1y4EQYB77rkHL3vZy8zfPvnJT+KWW27JtP+xH/sx3HvvvQiCADt37jSuGWL4xFEAqVIgeX4DYDodmadElyw6uO87LsZe/uMmHVmrNyg2yDAJbjuSw22NAMzBkfXPSz4fpwXWg23bMP1vXzNiUGi5AcJWElQXrIHIH8kEvaUK/HMV4PddB81gKllnY3W2L0KYB08OhomWBwnXpCPr78vWIQIACcuZ46SOjDK0OySpCaNSVpQ8/MdGhEneNz0twhT/kBjXgdr/q9bvMn/LuyeisG9Em3YjDSZogSo/e57Hqt/g8CbOSuvqeBWujJwTxhHlKYW4DCDVTFM/SmoBubLYISDC0IhlsXQgVGBXMhf9Q0VCn9omJR60eiq1mmgXpyPTIqJ2wpx+hdle7jZwtJs6RSTnlhPGwWjDw+cnxhGq+i2hPzEgwjR4EsyZHhGpE4aPYDYcTAMm49jUhIkcKwVfiaAl4hhCBbAn1iXOoionjOzNIvLHzfvZsdMRzpYXH3Oj4xknjHS8ymCUPk5+y8NsM1CfcRHs3FPYPlLXitADHjsjWXbwj/64dP1LifkG9+3zuNCRlcNOEcjn4Jz5v59ORObOZH26rZmaWeA2U70IrSgVeDf3phCWCMmAde2QkUnxxyu2V4QcYWOVeX9o4wtLU4YFTz5pXp/5iY/nVlSyj8Rw03sQBLE8yYgwVcJ2FJnfYU3+vY2u/bcsRRh93bT6rusTDosdP/1a7P2vv43+tm1DXS9BLDXi3HNP5XVG2YaZ62Ljtb+fvJ7n5J45M8QUrwRBEEud/LV4AHXvw5zhug+JU49lJ8IQSxfJ04Cb32hBODrftY+ZXG0eIBVhGo4LxphJRyaLgrWq3gMTMbzWGJjroG/VA7A58Ed/ZNbB3fRi6jgSgZv0MfJGMzeXQqUoE0osaLgO/CgJdudrhNhOGJ2OTDLXOHkO/c1HMqmRZMY5E0MaEUY5I6KC1Fi+Tv0l4cjqgKQw6ciS9bb85LvKb+K1Eya5afc9Fw0lOA04YcLAHKdWRoRR4krOCaODvgwcjZEJHFeF6sE80888+jDEpiZM+Q/bBQGHcLQTRgkTkhUHp+PYCB8xHOyWF4CpY/T0nw3OVhBK1NHHu62cMJOsXeyE0SnvnGSbV42M4l+v0tvr4IYn0tzuknMjQjDXxXjLQzNuYqp1BACSOje5h52ROBkfzGPwomRb1/S3YCYcTJMk49jMwG2104cxyYsv8dyKpY8bEWY808bO+S67swgb6d87I5sQTZenaxrrnMiIMADA3RZkPBjET8Qy5YRxumj7aRqy7r7BtGMAEE0mtZS+fiXDGeVZ0ZYkk5+fZx2YyBZh5lATZh5OGCkl3Hk8YxeJkWVM9yO8Yt9D5r1wPEQV/TG1wCSHYNXCs+Qc4AK91jprKSu8lgJAuHu3ed1YzXD6Bz5graxErCYnDEEQACJLPK6coW5NhjDLKkQY44RZjk9i6v4gI7w4C7MhtohOECuReTlh1L0S8z1zzi3U/crU9YOuc4IgiJVKXHMtNaUBFuh+hzh1oBFEDA0Zdsxr3/dMcLrI8dF87nNN4FU7MrQTpnDmoOWE8Zut0mBbsgJhnDBw0odnFwx9TwXa/VFk8pEpp4p+nvQ9ZtJdxV62nom0asIIMIw1PUjhGXcIdxs49N7/k7a3AuTS4SaPpN5O0ekgT9RanfTHqgkjguJZ3lq1d5iuCaPq1QhZ/GOST0fmeKmDw2nggSNp+rWIh8bx0bRFGNU+74QRViB1tOli0870OJXNytcxUD6HdGRb+juMCOMrEabjdAvTNNnCR8QcjLbXGeFmQFgDIMI+9m9gpuZMq59E9wM5Wijy6P0Cluz/89ZuxQ/Oi+ApMSe8Ow38JnWElKjiuljV9vFDR7eY8Rj5IwhE9vj6kRL6mgwQafq0qekCJ0wUmXRnq0ZbYND7vdiBxHkq1IxbTpiPv8qxG5mX4fZt6LWttH/eGGYmpwrXDQCtfpCeTOoAx14Tk9cNpiQTnU56LVi1FhdEgUljd+ADf1u4/nhGudQ8IFpmk1FmbvrmvNrbaQXn4oQ5eiIVw+Ma0SaIBZpy7jMou1aNlrrA4Uw/xkScjumZsa2VNu/4yGEAwFgvFWHKhGfteIksYfC0Q3eVOmHs3L3s06/B2Aufm66rbNYTOWEIgsDcnTAyjgfSzFalncUydsKYSRrWDPzWpZcszJct1Cx/glgixHzuThi7JgHT9zac7lcIgiCeLVFFxgYAgHpm7AWTuO7WPy6t10wQdZAIQwwNFqbBP89zE8EBidjQOPfcTNuJ17zGFFV2lRignSRFIoxUNTyY5HD9Jrr33YeLtn2qtC/a0SHcNGjowkFggt5ZJ4xORwY3LW7v8eQ7Y7edcQUkTpikzzFz0PJdOMyFG6v2Xgvhzp1pe2t2JHOEEWSqRBgjnljpyMKCYvIAwNUPgGf1XdPP3cgn26H2t5t8v+82MgLSR+7/SLruKMgcp5aqN6Pbx0F2/UL9eEnGMdLwzDEFsrUqbEK1b2NVQ4ihPKq+zz3TBPe1O+Q5B1z0i9KFxelxiqSDszdusIS19mD7qIdeU6XkksI4YRpxsRNGp5jTV1HfbyH2gIZyUHmH0uMq4zQdGXMcTLR9rEIXfeX6iLxRTPaOZ9bPhBKQWg78MBU8ThwaHC8ySsdYq+mDq8LmjixOeadFGIkQf3rTYwASEea7l1jBDmvMd48fRa+dug6414Lsped7noZKiSXQg0DSX+62itMS9nrGKdbY/Fys4cKMr96ufYXrF5OJQNZvMuw8Le0znx3cN8ud+aYj682k14nvf3lH7brb83DCdGZTkSP0qwNjMZdwrABkZ+z0ShGmc19SB8vnwqQO5CU1cIRyRkbWecykNCkF83hr15rXv37WRhy87j+b90578FoAWIEOgpgHccgxeahb35BYNtjXrbDiuiAL0pHJipQV0jhhGG54YXI9HX3JwtR2GDoiFWH8M88EAPhbtizQl5EIQ6xsYpF3wlTcf8Q6HZkHqOsLBQIJgiCePbU1YdTkmYend+Cdu/4Vf3Tjb5+EXhErERJhiKHROnzAvPYcB3C1qNKAf9pp2cZSpLVJlKXPkWk6MpEL8Eo1o5oJDt9rwGm2sHrqqeRvKEjn5SaBNeGlN7Ij8Sz6vhZhxrLCinbCKCHDdVInDPdamLn1O1bXUydMDIam56DlSHgqHRt32xmnjj07UjKRVB9H6hAS3cGAjRYzmBRg6oIf9IuD3vr3wnWyThhgsIis4HHqOmgkfWpAoOcn+1e4TcTSEk76ndQ54wAj6jPaqTDohNH7lGO04YJBGnGqO108S/2EEgy4SkfGpJc7Nil9mTpYtBOGwUMvLhCoeGyC+yHzsLo9Win0IepBOsnMep8FpuZMk7fRDYoeiLJOGDgOYtcSqGyrgEhdOY7rY1Xbx23yEiMKxv6ocYQZpHbOAO04xKrJZLwf3T7oQJFxZMS9sXYTsXKhuXIMfHbQOSNU3yQL8f19kwCAVjwKJotFmO17jyDy05oxsduC7JUHGpkSf0J3Bp2mFviaQKs10Jb30rpDTb8F32sah0P/vBcUf8GBvQCAyVEgsHSmJ174wsLmUkpMfulL6D/+eGmfTxrztDBnRJia9GIA4FizeETNjJ6IZ50wZSkDNd3Z9NrQCmRl+1gISDd7vKtqwkgz/gW4dlGW1bUKAkhkHW3cbUD2i0UYPZaPTgAPtJt4D99v/jT1r/9a/BkKahDPgOv+6h7805/ciQNPTS52V4ghYc+MLKo/p7Fdz5qqWmh69rpgwL51yXXYGRl06S5FtHMxPngQ3gaVGniI5SMy94DkhCFWOM/ECcM81+iTndtuW7C+EQRBnCrwmudgnT1BPzp/+dD30fipqwEA4z/5kwvaN2JlQSIMMTROv+NuAEndFsdhkK4Krrs+OrffnmmbFLdXrgplp2ZWgPzxH/rh7Exkk2Ymhuu4gOuYuiQMPmRuppwJslsFD3wp0PdS54F5YJQSqQiTnhKH16aix67/8ftp3+NUyBDMRdNz8NPydhye0E6YnAhjF2V3BOBmnTDx0cGiFlLdgDMpASSvpzqTA+2A1AnjqgdVx2FoqO3I38jzOEpFGD8Rg9Z1J9H304LyseVeYUEa8GeMYaKV9FkLDVFOnEiDsgIjTbWdbhIlP7ijOH0Vkyp9GrP2WdlvoOoaE7HpA+CjW+ASsvd7KF2sHmnBEUm7ohQhMupDOBMAgIYbGRGuHbcw3SsSkAZT3l0QSyNQsb7ltIq5Cc4w18NEy8dxNmLGY9iYgJO7HGtHEFOi2tTq5wAA9nxzUBDiYZqObP2qUXTdZCz22hsKUzTFUMcGIboMEJBgcNCM00Ln9iEIZmcT95j+Pq8J95iVbi3fH5YEkjr+MQSeEqXcFiY//enBtt30PFs3uha+m6ZQ2954fuH6WZD0f3IU+PqV9T9js9/6Fg687e3Y+bM/V9t2wZlncN92v9Q5YaSUYPHcI2FhLDKjrkz00PR62fNs/xMnSttGXEK4WZdJVDG7UzvumJTguiZMmQjT70M4fibgyd0GRIlQrWeJ6tR1t4zWBzrJCUM8E47tS0TvJ+4qrmdFLD+yTpiK4GhUUBOmQoTRQoNkgC7fVlQ3bSly6C//yrw2RcGHWMQ72pcK5aVpJglihZAvBl1XewoA4HrofIfEF4IgiGER1T2jqxTrdhrZ3hlJppDlMomGWBqQCEMMHUdywHHQCFcDAPae8crBRlxYxbiTh1TjIHBbSQovO02XriQuOTzmwRkdNSIMkATgbMyDsDXCXSmz6chM49QxwSwXycF1zATUY28EIlBBfyFMewkHnuvgIDbj4GpLhLEepGWUrh9MgCnxQ6hZ4sFT2wf3T0e5QqyaMFFU/CDK1YOvdsIAqRsmL8KIKDTil9NI+rF+Yiv8WLkVnCZiy1XCrYdfx2WYaCeCiq4JE0xmi7ObdGTgaPtqH6ltv+Wfil0ITB2k2Em/V5QEOnQTV4TG1cLgoR8P7hsZBmYWaggXrsNwaHXymVDP3LSJehDKCdNocFM/phGPoNfvDrhzzJixYiyR68BT4/ic76VBBPAYQu9310fLdxBDYqaZpDzrjmyEO3A5VqKNlxzXdccGU3lp4jAydZDGR0bQYyro7I9iujsYKBdCO7MiSAbMqFRwa/unWxuYbm/cmUmES/3ebcHrDzps0p4nNyKB20PkWk4YDDq/4m7qXlgzsha+10bEppP3k09AFARgnCDZJ7Nthm1n1s+Sffp//M/aNkuV+aQj45HIyNHtLdU3hHnbdZ1zpt/NBgh5heDDhYR0s+nworAisGBqgwnjiuMlgQgRhAN1nYTTgOz1C9vz40mqv83WqbD+vycW8rFXvKLwM+GOnQPLyhx6BDEATd5fMWRqwlTV2YqjgQkejUuLJxIAyDhhlpsIo6+pAFKnihyeezDcmabSPPrRjw5tvQSxFMnfi1U5YUw6Ms8Fn55eyG4BAPjk5IJ/B0EQxFIgqpvIqESaTOIQ9Xrqi19coF4RKxESYYihod0oTHA4I6MYmToPABD7SbBs31vfmrYVab0OR9Uw0bU0Tqy9CADQ3/ZYunLlLGEyBmMMa//TfzKuBgCmWLtZPxt0tjgAen7yHZmaMHEAHfB2vPQB+tHTZaaGCNeFyIOumbXN1ffe3HgFBEuDzeF2S1iJIyMGSEcCXtP8aXZ0Cw79+Z8jj/DSgCR0uq6gRIRRM6g8W4RRtVvyQVsehwNOmObmyxG4qRPGdvFwKyDAHIZVWoRRQsP227JFxpVeBMk4Gnob1HE647lrCvvvyCIRpvhH0FNNHB7CMWnTPPSigsBFGJhxEMPBay/bglitYLq1daC5iANIJxEaGk3AU2nUfN6GE0fo54IvOp2enUUsch2rvk56nEUUQar0c47ngTGGDRM+Oo1kTMVuayDlhtRjUqWAO29HkjaJeYMPZ3EQGldAu9XE5vXJORf6Y5jpDzqQhHLZCJYc9+Nqv7xy+38DV/20HRt88gS4l6aW4l4bmx+qulFJ2m7yJDaKWfWZZNnTv/t72b5YqQdbzVEEfguT/p1qiSysmeQGyXd31S5+6sJ0nHfuuKOiX0D/0Ucr/36yqQvsB5Z7pDIAiEFnGq9pn59tyWty4QY5J0xVoDniYuC6HIXlAUZzPkkBrkTEYPee4rZBH1FOhOGOX+peOfhng9dYJ0qCiLM331w403rfm988+L2LNCObC4lf/Ls78DufvXdRvp+YP6TBrAy4kLAnqVc6YeJBJ0xc8Ptl2gtdE8YSYfjyEGFQlC5siCK1tO7pilL2EsRKIp+OrPI6o+9zXHdB0qbm04FHhw8P/TsIgiCWCnZq7Xx9rjw6HZnthMFMep9HojUxV0iEIYaGlDqdDMfIj/wwJs9JUpB5SlyZ/spX05s72wmj05FZs+hit4k9v/Zr6crNDakqOD46CtFMh+/M2BmZvmjRg1n1F7470kag05HZIgwPTZF1x09tDTe8AKbOS+LOSQKQsjdjgvtdR9XJcNrg6Kdt7f3CueWEAeCnKXpCq85G5jOqa1OrHXhq2/nRY4Vt9c37sXgH/ssN/wX3Hb4PTSVsVaUjY42kn47r4en1qQiDjAiTvnaY5YRRDqG9k/twsHMw7beVjsxX9XXO2/FVAMDIRDYga9ar05E51kN3iQjjxo75fp2+DsxDPxpMR8bDVJgI4WBV28dEmAh8+9e8aKC95BEkS6L6fsszThgGB+OIMJULQKdCX/pLHPmO5ehKt1dYAQXHT75jtAmEKm0YmAMOyzUgJYww2Ei+pxkkM95k7Aw4BKIgNP0ZaY+iPear/oyjFww6VriarSt0CjhrSsfhDVdYfUiY7WVn28VeuzT9uxQC0lHnQJPDcfR5kWx357vfzQSz404aYGm1xnFOaz32rtOpCX1ETz+dXX8cGzHOVTWfbr8qDb7v+Y3fLOlZws6ff13l3086NTd8tvDSryrWioL0gDUiTBALO5terRMm7OUChBXNYyEzQiRQXWyWr0pElamR1P0nveJrhuj3jbhvlrkNM0N0oC8HDw4sk2HqwIpPTJb2K/OZfrHTZqF54tAM7tp5HF978EBlmhJi6cAckmFWAgMz1CvciJLzAREm3HugpDUKnTBimThhMpNG1H32MJ2C0rr3HHvpS4e2XoJYiuQDf3XXGSBJbeyMjw+9L53vfz/3hUP/CoIgiCWDnQ4yqnkOLnLCIEzjGHX3Qc0LL5x3/4iVCYkwxNDQlx1HuVXE+CQAYKRbMItGcOOc8XSNFCuvE88JGfphVSK9MXWtgGNnbEu2L44OkGfzcfcz6chsJ4wWYdIH6Ev76zHVtlKMGdEmsgSkpL3v+nh6fXHR96RYq1U/pJGKMK4YFA8AQGiJ3ZEmIOl++57Ctjod2VcP/TEeOPIAXv/vrzdOmPyNPI/DdL8rJwwcD52mFmGaYJZgIK2gKXMZJlpZJ4wrmuhG6Y+PeY5gwtSl0WJG0C0OLrgy2TdzEWE81cSRUS4d2eB+jIO0X2G82tSzKUPyGFAiTGOkkXFanR0ngdBcz9V/6S8x99xUhLGLhgeWCKMC0w1fgjsRhCp0EzMrYB0HkEooYcoJ48VduMqdM3UkO1Mt7Kd9HWmPojmaHKfQH8ODewcD0ALJsZdKhHm0ma7PBM6tG4mnoxNqkQCkSMQRfzxN0Wevu9s162BNAdfRzqD0nD72if+Xft+kyhElBdqtcZw+fiZCX9XucXzs+o+/mFk/n0mPg99OtuPO9cXB+qTPS/sJsq72yHzSkc1XhIm49kQllKX/0jz5dFaMExX7NhFhsselzM0HAKKdjJmdpwFToyrIMFIiUgchYi9bb4Y7/vxS+Ujr2jbHgLnoD473k4E9S3a6X/ybQSw+9rXGFueJ5cuACFNTE0Y7XjXCKX/Msp0wYlk7YdT/NQVt54X1u1hW64sgVgr5wF+VE8ZOR7b+jf9t+J0ZmBi0tO+hCYIgng22CB7XZITQaVdtEUbE6T0KY9X3/sHjxan5iVMPEmGIoSGlTj+lRANVy0I4+Rn+QHzihJm576kUYKPdNFgcezkRxgQHsw+orV5S1L4ZTKJ9xRXp17BBEebi5pZUhPFsJ0wAfSq4lgjT6LdweCIVYYz6HYcmdY6rHCee28DBNSUiTJTWA4HD4PhtHG8fyLQdSHOj73mZNPsT/eKHc52OLJSp6NAorQkTW/s9FWEilY5MOI1sGgjrh4kxmHRkzSBJceWydaamDwBIJR5JcPhKhPHjZJ/3O8XBQ1+Nm/PCVlqQu0SEcSKd8i4VYWZbfqETJrb2aYgmJto++l6xmwgAeBwYJ0xjtJlJJ/P8/ioc72SPkd6PrXjSLBOMoREm7zsjm01QmFsz6F0lfp3lvxxgqRslcqygMg+hnTBuI9nnDBKNMNnv/dns9kaWyNNotnHeWauT7W5OYOfurJMEADjz1f/JevZ7An0vCbDrc8+OsayJV6lXswjc5HiGjXHMfOPrA+uO9uwBd5L96DYBz02+w3ZFzN56q3kdHDqie4VmYwyjoxsRO8XnEgAIJcL0faDbTPp61C8X2PLnVvPii0rbLgo1woEtvORT4uXJizCy5mYyjAVca6SLmly44152P3e65YJAzAVEzgnDS+paAel4E0yk14GSW5SidGSiIh1ZEUxYrpaCm2Zn1aqBZTJYHCeMnZKuF859G4mTS2ydnw45YVYE+eDofGvCSOaWNEbGCaNMnQjuue8Z9fOkY90giBmVcnRmePUpMnUVQxKeiZXNoBOm/DrTf0yl6nZc3Dpzf/Laq55kNh8GJrPM476KIAhiuRELCU8Cvz7dxH856lem8pYFThghrXuUGhGGIDQkwhBDQ7g6HVlygXL9QRHG5FLsdI2bxFcB1HY/DZDnawmkM3OyN4NNFZTefebVJiUCkLpqHEuEmZQ99H3thBlLZ63GaToyz7qR9cc99BuJus0tEYZHgem7pwQI3/XBHeXWyQeOeSp8wAEcd8TUP9E1Zab//d8zH9Gp3cCY2Z+SFZ+uvGD2oecnD8PFNWFYdluZi9hNnTDSCpSaoKYUYIxhou0hOk2gESXrX91pw7H6pQ+ThITrMjjPDeApp8zhE0dx98G7BzdAiTCedIzTSZQEj121mIHj+OmqFg5WoVfghIksl0bT9+G7DrZv+WcAgCOi9EFG7xseQirxoDmeCCKrJ58AAHScPmamc2m91HaLRioYup6H8Zm9AIB+ay2k6oM9g95TNYH+4xXPw+uOeYhZMsaOjV5s7RNunDCO5eDRNYryaaGiIN3+VmsUZ5+xOlnuj+GsL9+Y3zUQKvWZFoBiuNizPqnDEjYmdCdMe95X55MM0Gw5qt04+IGHBtfd6UCoguxeg6HhJwfNFlaFJUpxlTNfgqPltTHaXgfupE4YIL3pAQBxIhFeu03gCK/PFZ/PJx/u3FX7mZNJlXAgpXxGTphIHTtZI6qEPJuOrMytZvqTE3VnK0UYaa5vGlFRUyXSN7YQEI4WYYpvZkU/ME6zjp/8Bgi3gf5Dg+OxDGbNXCq6aW5fcgkAwN2w3iwLd++e8/qHiT0rtm4MEMPl4e/sw/XvvQfTx+pn48eWCFo3G45YHuSdMPOtCVMlwthOmMt3LI3Z5lKIublHrfHdf/hhAMChd717eP2wHdkV4j1BrAQGxN4qV7K6bjy1/Qd4992q3t0whZKcICSH6XAjCIJYYnAu8YLAwwbhYEIw/OBrO8sbF9SEicatZ1269yfmCIkwxNA4sXk1AJj0WZ5KiaUDsgCMjQ+cp0KGFgP+8KfQ7h5K/pxLY8PMDao1I/i8wNSCmR07Iwl6qIufTv/VY2ng5LmjZ2TSkZkSNDxNR+Y10gfoXd4UQjf5fOS1zY1oHPVTAUn1fYRJSFVMujOaS40Wx5n6IZ43YlJv6Qd2PpMN8BsRxgEctT+FU/wwXyTCNEucMHZ9Gk85LOB4iJy0Jkyra6cFU2mB1M4ab3p451U/nyk+bz+w630qGYfnMDxw7vONE6Y7G+I3vvEbgxugRJg4YhBOsoKwwNkCAK4aB0zGcFiyXleOIChwE9gB37ba1mAkcVEwEaN7dza9W8QDSJaMu9b4KMAYNh5OCmFvmp3EC//bz6Fz++1pt9V+jMY2pithDg6uToL+sdc26bpiXX9CcriqJtBEy8dLT6yCj6SfkWvN7BepCGOOE1IRptvJpkUyIoyM0WyOo6VEpMgbwabjxwf3jQqOC5bstwgeQiXcGRHGOq6erlfD+miPJ38P/XHEbPBBMe72TPDdbzjwnXSsaOzaGnFGhGlhpDlhREotwvQfecS055YIMxfiI0cy72WvV5hGbbGoSqGVD/jVOWF0OrFA3wPW1XiJs+nIerPVwa5eTvyrEmEiLgau4yIs39ZZVWfKdsLICieMTkfWbSTjlqux0nvo4dLv6NhjxkqjOHvLLYPfoQIRm/7gbWbZzE3fLF33QhJmhDiqCXMyufWzj+Pgjil89/NP1raNLJdS2UQCYnmRr8FUNUO9SIQpcnOmf9TCM3D7RemD+2Kl0BRhiCdedBWeftPv1LYdffGLF7Qvdk0YcsIQK514PunIFAfu/zK4vmxImZms9GyY/c5t2QWSfssIgli5REKgbV2Cdz50tLxxQTqyWFgxvDncv8mSGBdxakEiDDE09GWn30wuUL4qKJ5JR6Zru0hhhIyGn/y9tXm9qZEicnm1mbmoWQLBFoHnPv5ZAMDE9C7AcXDeTcmsf+2EeayXBtp/6fSXI1AijHQ8mAwvcWBcDaZOCoAGayBQIkzstc2Fl8ehEXm0gLSW9eBxqxC75Q6RMbecMAxNr2Fm+jdf/CMAMFh8XL9w05owZTMqdUGxZijRDpLXDZUmLS/C8Cg0+93R4pfjIrZEmGv/Lq3ho++9mUz2+/PPWI0n5WVw4zSwHvc6aXv1qyQZh+swMKdphINm3B5ILSyEBFMizIjfglDB13sOFte/8UyMiwNKsGFoIowG0wTFOuArBUaaSuxqqO302gMOBM77Jn1Sc6QJ5vvwVT2bNb3keB145zvTbdV1hDxrrDounl6ra8K0UyeMqoXBZAxPBY/XjPiYwihkM3ngEbCCNSI2Iow7nqYp0/vy3bf+BYT1YBRpp42M4TdG0Rxrme2c8dyBQJJORyZYjJGGiwANRMpREDRWq3VZ9Q2kSonmhPDHkn0UNsYxdXxwtkjU6SZF0gE0Gi6aPBGB7j5/NF1fK3XFBLPJPpaMY01rDUabqwZESjuH6uSRXQASEeafXvOZdJ2v/5mBvgDA1PVfHFgmrLoyJxtndDS7oGIGY/787VcUtgfSNH4BU8dOVgeDw1jAtc7Jfq/aCTM1kxVpuiUpEgGARzy9rkbJNUJU3HgydTsimDDXgbJ0ZCIIjLOqq8TDIsFu4HPWTfNkmKbOOfCO/13QWF1frLRSIy+8snTdC4k9S7ZuDBALw7H9ndo2cZCea5OHqY7FSiAWcw+Oymh+ThiTjswBtm21RJj+4qQ9PPrBD0JMT2P229+ubdtSTsGxV75yQfoyed315jUFLIiVTj4dWf6evYi/PPcIuH2LNJ+aeBVMfTF3zzwkcYcgCGIpkp/MHFdcf4vSkfE9t1sN6kUYvogxCGLpQCIMMTRmpQo4K7fKSCsJ1kZWkNrM8BMidWT4Sbv2+AQcNRs6n8YGxmGRXhg3XPBqtIIT6jvagOOgccYZeM6/fQHHx5MHYd9KgTTeWoXYjSBl8h1hpIZ/HKTpyCwnTIM1EXqWCKMuvHHYNwE/LcI0HQcHJrabz7pbtqbbHEdGtGEuQ8NtpOnIGsm2i07WCdNRu2lWBJYIUxKQFBKQEp/+vxyf+msOP5JouNoJkyvUHUepA8nXThgXkatEFadpUn4BgMw5kC7ZMgEOL+OEif/lS2l7I8JIeI4D5vqmrQMXjswGJEIuTPD1sk2nmeCrLIkzahGGgUO6XK23ic3dxwbacjN7kqPdUOOpaa140xnZ9nFg3BrN0RbguvCj5LiE/njSLyV2SCnN+OVe+oP7q2ufD6CrlrfBe0kwxRQklxxNNd7PWT+KKTkKKLeWPeYlj6BrwvgTo9j5Ez8PADix9rkAgB/d9Toc76cOl9g4Ozh8bxTN8bSg+VlTMe458GhmW4VKu8ZZhE0TSrBRH+m1N6hOWCIMdP2gEOs2JA3Dxirc89B3kSfudo0Dotnyceb6RHQ4Y+Z5ps3IlWkwO+opEQYcba+N0eYqRI6qxeQmApS36TTTPjyRpC3sNhmet+H5OF2lLelfpsSqnBX4+Kc+NdDHhSiwfvgDH8DuX/+NwfpO+e/uZIO5VenI8qmn9h6vTr+mnTChfXNYkZIs5NmaMJ3Z6mBXO1fnIqxIjRX1lYghOXwlwqAqmCa1CCPT1I5lTph+YK7BgafONzXmpCjvkx2wOCBqUtnpwIPjonVxkiow3LO3+jMLRETpyBafOTxY2U6YXQ9WzKQjlg35dGRBxfkn48hMnjDL5pKOjAHcalZXy2uhOPYPH0/7UDPe9XXWW7duQfrSf/DB9LtIhCFWOPl0ZPnrjsY+LyMvEXA1U1/9twXp27AcNgRBECeTg1N93LG9vBawJu9ELMowYyhIRxZbr+fiZK7KgEGcOpAIQwyNIywJnOq6HuMjSaBXOu3UGaIfOmPLCaPSLTX9duqEcbNOmLRQffoA7J/1IvhhoiaHjQkzY9nfsBpQD76v2PxLpn2zOa7WlQQEAx2R46kTxm9YThinhcCIMCNpOrI4MMVXPVXPpjtylnG3AECXWxfYODbbzxwHvuuaguh6lmX+oq2FiT6LrJow5U4Y13JFbJgGjjm3JNuYS53BezNmPa4uZu54GSeMjeS6zo964HYdvOCcjXjkTCXCeC3wj/xj2l5/HRNwWLJuLcIAgCey64+4ME6Y81dvgWDabVT8I+aZzeHIGFDiwZmjXP/IyRgjSlxregBUAbVeN/sZHvaNCOOPNMAYQ7uXBNJ67fUQzElnqHJuhLX98X6zjp9yJnD66hPmfTCjRRhLEFL7mDGG5thaMHXe6GL2ABBbaU2aqycwcc5ZyXKrGHnf2uZIpTuTiNFojMCxUpjNjJ+J3775V/C1HV8zy3QAmzsxNo4n3/tz6y8DAITNVQj9UfPgldyMqL65EdZvSkSVPWe+Cuc/uhl54tmuEZRa7TYuePV/N3977KcSt4q0zo+4p9NQJctGmqvQbajzenRt0t5KTRJNHVP9TPbhiBp0/btVAEnK0psgVxVbF7PDn4Vy7CMfRffOOzFzc/ks4sJ+VdyM5c/fxw5W91s7YUKWfk9VgcEgznpNupU1XsRAerMoLF+3Tj3mxn046rqOCheHccI4wogwZU4YGfRTl5S6RgvHhwTA3PL0P3bAIqwQawDrWDkM/UcTEfPohz5U+ZmFIivCUEDkZGE/iM1lr9s1YYiVQX5G+pxrwkhd72VuThj7gR4116YFw7duquqED70f3IV/jCQRhljpxOp8GlUZJEqdMNakHcGy9zQH3vGOofRl1c//fHbBIqVHJAiCeDa85K9uxq/8/Z34/o5qISbveI6rnrOK0pHN9zaIrqkESIQhhsiGOAkQaxFmZCx1oej8/UaEEdwSYZKAree1AFnthIEVXGy0x9EMkxRK3GsjVvU80JuEp+5MV7fTosqN1moAgK9qAUzfl6StkXFaE8Zvpt+7xltjZllH/liajiwKTRBbp1ybmbgMkkkIlS6ty63i9pmaMA4arguh0i3lL/waV13dt7bXwhHV6ci4kGBWnq9X3SuwL/4GgMHUNeF111kOJKsmjKsC4W620EZX6hosVsFh18cXXpKmjcukXjNOGAHGGKTnw5ECTCTb6/O8CJOmI1s7thq+2n+zJTU7HHVcGTgu3Xhx+mMYD7bnpv4Ex6gSYRqOByYS8eLv7voYYpEGwHmcijCNkQbgOGj1j0OAQzo+wsYE+JQqAt7vm/140for0n1z5ouw9swZ4+gKptT4USIJkxwtK+XdrDMGBiVosXT59NQxIwxivIHo1T8FAHjOU0mKjr7XQc8qLM6N+yKGl0uH8tAlbwAAfPrRT5tlgumaMBwT7WQcjI+cjpab1GSy6xp1wxhjs0kbx4uxdmOaTuvJ816HPHE3TUfWGmlj1XMuSNelHW1Ben7wfjKWtMvN8dsQ3iQAIGQjyfiyHjz5k4ntN2wkY3Oteng9cNYL0k6U3eAol0zPmmU7bCoDRgUzCqucMNP97LrWjTZKWiZoJ0wMgEPXsJp7OrJer6rGi4Qns06YqMKyrYVUR3JT16pq9o+vhG2JtCaMEMWCWnzsOIRKqafrdoE5kMyDt2njQHuNHbCIq3ellY5s8W+TsjVhKND/bHhk/xQ+f/fexEFaQ8c6H3pzcCfYThhiZTDohKkYB5YI46jJJ3N1wmQurYs085xZIoyoqcOiJ1KwKpFpSMio2l1KEMsd/SzYVs8qpWKvdW0QLOvuHRZ6slLRdxIEQSwX9ESq79W4YbgQsG/BoooJVUXpyCL7w3MRWOiaSoBEGGKIrD+hapOo9+3miFVTJQncmodOnoowrWYi1jT8Nh7dmjy4ho00nRIA6+JoOWHaq+DxAK4KRk8LJfRM7gGTKuhupRdzm0lB8bZyJfS270r6EgfmQbnZSkWIn1zzk5hpJimfeq115qLJw56Z3ej7yf863ZWezc+tctcyTAP23OHwXAapnDBmpm3uou2oU9P3/NqaMFxIONbnf+puiR9/MPlMPmAQf/W2NB2Zp/aq45pAZuS1M+0fUWmhpEyDp1c9ZyO6jVT0yBRcV4KKDqhLNSvdFclDtCeyIk8QxXDUvhobXQWpgq+dfvFDt8n85QiMtEbhKrHjqcZTA215lNaEaavj5DsOmEy2KezFONJNi7av7zyJWDth2j7gOGCQCNzUbaURs9Om7tDo2Jr0SzdfhknXgcuT/Xn0we8BALrbkv41ohhtS4Q5Y+M6ONrdZaXR6hydNK97rQirJhKBc3xmDwAgZrOIRBok0YKTRAwnl7bOiY+gGY2gb7lJtOtGOBwttW9m2AS4TI7/oxf9mhmTvZCDq7RgozMdrNqQjpHuyEbMfO1rGH00TXcWdy0nzOgo4HjotpKaR311dbBz3gsVuJTK/QCvBebOJIF4xhD545ngPT+RuJNCNZSevyFx8DwCy9lk3eA0zjkHALDuDb8FPjkJADj4x3+CBSOXDi1DkQhT4Q6ZydVc6dUE4LUThrO0elZc8ZkwFrCvKv1uuUgSC6GT0plZP1UCj1AiDBPcXMNcXh5MY+qOdlPMzHVAMhfh9u0DbfnxY5DGCZOmFeOunxH4BvpkHZrOxa3SdkljnY4sez5177yz+nMLQEjpyIbGr33iB3jrdQ/iG48crG3bs9LzTQX16QOqHtyI5cm8asJYIowr6kWYMifMXNJZLARjL3kJJBi67Y0QQU1dGrUfmHcSRJgaQYggljta7B2pccLYqcE2dM7F5ukLCts9K3L3qZSOjCCIlUwsJJgVaaysyVWQjoyzzA1c/RfSNZUAiTDEEFl1IrmojM0kD0wNf9Rykqg0SlrIiCLzcNpQdVGa/igOrE5Ej1AXB4cWKlRaLCe9uLlKvGkGkwCAWS3CSG5qj/hWWjO3kaQjc5T7Qc/Wj4M+9KnQaKYiwdjIZkglQgjHM+nIeNRLXTx+8n/TS/7XacaE9eAtg57Z1q7swncdCEenI1MX7vw1W+r0ZcykIxMlM7K5kKaN5jkHlAiT/yF5wXNN310twjAXv9BNUmpFjfFMV1y1H5klfv3cFVvgODGYEgGKRRidyiebmqPBsyLMse6keT0xuh5aZOv0igOpjt4vEICXpjqbcQbrOwjjDkndHo+KWTAlkGw92gKzfjjPnHkwLSjf8iCmk8LdkRZhVF0YBDOQM5Opo6hhBXO9Jnb7Pvwo+Y7eHbcCcYjOHfer/nOMWCLMCzZIcCVY2amXesenzOuXnHUVLj9jNQDAj5PtHIs2ITqR1oSRoXbFpIHCCefLyZLmGfiNu9+D9XvOTPeNcnI1mgxtNYan2TgmvEQsCZprzI1EN+SmCDrzOdadMWZm+UaNcRx4+ztw+qf+0ThA4kceNmOiNbYaYAzcS7Ynlsn3ijAV8bQII4wI00QTwhIGRzB53fVGiBFOUh8mHEnWdXorSVl2nFtjwLrBCXfuBAC0L78cC0V8LJ1lw5xyEaboYVaG5fVp8jeCvZqZ9toJwyFNjtqqdGQhzyb8CvoV9VSENCKoVNeOquKFiJPzj0kOptx8blw1ozlZp2AOPB2cZG5hfQQppAl2Rm4AAX2NbODIBz5Qvg2O/bomHZk+VjlRbfbrX6/83EJgO44Grukl3PT/HsH1770HYb9ePDiVODqbnG/3Pz1Z27ZrOWHcOTxXxdb52RwpT4tHLB/ybr98nT0bGcUmVa2+NxElIozMzWjPXGcW6QF95Id+CI9d+J9x54/8Cb76sScr25raWyfFCUMiDLGy0b/r460aJ4y5N3JwzRNvxmu3/Q9E3mhx22fIwH3qHFyjBEEQS4luOPdnn5inz7cAEIxUhMcL0pFFrl3Ubw41YSgdGQESYYghooPSWuRoNSwRRt8kGidMbAkZut5EA6eJpBC6HdgPY4GGmkltizA6dYIWYbpMiTA8AlPiQdNywqweSVKTaVcGd3zIOEZ0Yo+p79FqpQF1Z3Qt3n78cNJfxzcX3njbDpMqqqHEF+0mEEaEsQIwQc8IKMxl8F0G7qggtih+gNWKPPPceieMlCbdj8ZTfc0HDMQ5m816fH32Ox7eMHPYfEd3w2rT/pwoEQzsANRpEy2MSmmCDNy1RAiebP8IS+rubIyT2caxnzibLt/3E5n+HOukQsLo6FoILzn+/ZniH0/tcGKMg7meOZbr+msH2vIDD+tXmGglYyWCwLiqBXPp3lbmhzAU6bjzm+m+XqPEGO2EkZ/+JcjerBkzGRHGbeDnZ2bhqKLfjoyBH/wDvEsvBJCk/2p56dje0N2OB1XfhN2XrhIUpMB4axyOwzC5egJenAoNs7enQRKuHtikJZaNyHTfAsBl215jXgvlhBkfc83YncYYLl+VFLH3wxnzINYJIpNOcPrCVXAchssfeDscHiL2RtBrbwAATH3+80lf7rjdiFnttYlgwhuJ46ihaojYbgUZqbpIjnrwa4wiBEPs6vHVROe738WJf/lc0rcHknWNB8lYGG0mqRM6Mg3UFKawOlpfnO+Z0vne9+bWsCC4xtX4KiJW7cebybbO1QkjAMRKTuUVQfskHVl6JxlUBOwjLuGq69JcRBimnFdJOjJVH4ZX3RTrmjAMx8a1O8otDsBxbkQY7sRpTSvHR7hjR+k3ZGYu1dVdsNKRrfvt304/d2Ky+nMLgF20dy5OGM4Fnvj+IRzcMYW9247Xtj8VqUwrpdvY58McAlGxVSNpLunOiKVPPhhaNUNSxpFx6DnaCVMmUuRFGOv/qhSVC4kUHAc2XwUAOLB7cGJLBnVvyRaoJoy7JnUYy5DSkRErG/17pEWYfBpEjb63jS3hpd9cPdS+6IlLmhOf/exQ108QBLHQfOeJNNNJnegRi5wIU5HQQk8MtEUYPmJlRCEnDDFHSIQhhoYWVbQro+GPGREmVk4YfSGU992fijBNJZ64DUx6Kp2XJcIEMQeHDr5bFy61Ll0Xxps5CGy/GSKOTTqvhpXjekT1gVk1VmQQIPjC+9LUaO00oN6eWIcDvg4QexDqwTj+l1stF48Se5QYo9ORSUuE4d1OKlA5DJ7jIHaT/RJyP7MtKWpf+l5aT6FEhImFBGPZ4OZzjiXryxdxjsPYBAV87YSZ3AOfpaknQktU0M4TuywxYwx/cDSEpwrDa5eE3W+o43R2PzubcmNna+Z9YKW8YK1RjHQSa33jwVxOYiRjh2nRyhFgngsvSsSeDb11A+357An9CquUEyaUAp5ywsRuK1MTJhbNVKCyRJhVM0mQfP/mF0MwB9Fjd0IGHTPDtdW0itm6DYxJgelW0q9pZxzYeyfk6sRF02lytPx0bDjtVZDQP+jp5Tjs6RoyMRwl4H3/vJfCV9sLAJPTaVowYYK0VtquF5yT2R+OtMakOr+8ZiogTsoRrHES0SxqjCNUKUCmZwPEXnLuOKPJmJldxzDaOZBs08hmAMDRv/wrte7U6TMymnwuGkkC4wGS49+57TZMf/3rkFJCHwLhqjHmNXHMcxHpwLpa3+R112W258T6ZL+PKHHMFmH0DY4dzPrOmsOImgsza/foxz6WfnW3InhVcOMlOp2Chgk6+K4fzOsC8ELdHHKW/APmUBPGfl/htOFCGjFWqLvVSieMEmSZ5KaulVchwkhtBWdA4OtzwoXsDe5PKUQqwrAYXDkLtfgXHThQ+B2H1liJLUX1LCmpfsdu3X8bVv3Ma83yzs03V35uIbADwXNxwoRWWrmwwt10qmE/iFU5GjSRJarYAksZwhLLJCcRZiUQ5Y5jXHFcZRyDK5epw7WL2i9uWyHCDLNo6507juHtX3wQh2dq0osBiA8eyr6v+L0x/V8gJ0zzgjTNEjlhiJWO/j0aV5OySsVedU8b+akIM7Xq3KH2pfPd72bez9x441DXTxAEsdCc6Kb3DbM16YS5ELC961OzFRM/rFp+msfOS9/MyeVCIgwBEmGIIWKKz6uAW6s5jr6XBBij9uqkEecmTZRu7+r0TCLCoYYWYdJAbhJ0Si5wjpdeJoUq3q4D032vjejT/wHCemBr+NmUIH938DCOTaSzrEUYIgzcNKDeSgPb4yMt6ERBwvERRqo+ihUsb/g5EcZJ+u+5I6ZN3J1J940LeC4D16mWRPEDuklH1vQQKz2qLK0FFwIuy6Y02vJ0sRNGRukPkTeihA4RwWECQKi+J933Qkn9DNkfjI2RDz9Wx9aakSV1SFfXORlXKaP23QYA2L36kcx6wiAtLu+00jpA3sxgvYYk7ZoS45wkHZlOz+XFzYEfPrH6PP1JTLT//+y9d7wkR3k1fKo6Tbh5c95VzkJIQhICCSQQSCYYbMCAMdh+McHhs8HGYHDANg6veR3AgANgbJLImCAQAuWctUq7q9XmHG6+E7q7qr4/KnR1T3fPvbt3UZrz+0k7d6ampzpVVz3nOedR9SMEhxvrOkVVRFbgvhUm58y1gvV9TWlHNjl4HLavvgKtFoVoNcw5rQTWNebV4AmAEanoud9fiE1+YAWrmblWAIB4VXBNLFrXVbupA9hWMDXmcHgEN2qoLVkPfRUYEhZJ6Q91Xi/3XfcJiCgy95cbEFRUf1oRR33paaZtUx3bmZkmIkXCuIqEufuSpag3JGHTqC1J/YYmeAQ4KsreL6xJImuKDpl2u3//DyCaiVWfcNLXWKSUMDqw3t6wIRW42nmSvO5qFbnNx+x7QE+Smsn19dFd/45//oVjExgNNyd1S/b/w8cL2+XakZUUq48NCaOUXEwUZkgCCeHCkNBxrCR4HDKeUrlFJYRNbE1SD2Gj3HapHZnKlOaxUfMNzUwVt1fTESaIsabjxEmdQwOW2P4wGiF21NilAp4H/vGfcn/hyy9N7jEuyifl7cceBwB8ZcNXsSs+VNr2WCOaY02Yw3uT4/zdjf97TPr0TIRd32NWShiLlKwKGEvSItgkTE8J8+xArO49z5HPvqhk8Tz5ve8bJQxh8pkXu9X8RbmVIMAp0MdFklk5jwv03/7y/fjq3Tvxr9d31s3LYvQLX0j9XUo8qv4T18GC//ObAID6xRcfcT+zEBZh31PC9PBsR9aOLEv+aug5pE3CbDrpV45x73rooYcenlmYsYiXbH3VLCIm4FrSltLguOgkYb4etBJL2Zz5HvH91N/d1hI9PDfQI2F6mDdohYVWbgTBgFUTRgbYBedm8abJDKoLe8ZtxDSxIHJGpMWUDDqptnZcWW1H25/t9Kp444ql4FZQs5oZ+F7YbKHh6wHUgdh6N9onvsj0JbCUMDXfRWQFulumdkNy2wRe2o5M14QRJCFXmBWwpw6B51AwVcMk4qp/HYO2Ij8qPkbPVjZY1M3NCGQcGMJ4x/tAZ9Y0swJ47pIT5YsRRVaoIDYjSZ9cbdmTqTlzPN1rkRnFShj4Hkb7gGpL2VGxKmy0wyTAeuCfPp27DxoygKbILCqkHZkq9D3ptXCoMZ3eVyr7JSwlzImtBrxIX5N1RBYJhMekrRZlIahV18MPE7uovUsvQrvtQLQTdVM1sK4xStF3wisQUUlQebyOa9gYmCJyBGJjYSffEHjdlK7/krwft3U9jeRaXlSX+7D44P0AgDC0A8rqBUnO7ylLViKLv3vgYUzfcoshSgb9JgJ17bYiBn/vg6atvt5bzZZRsjl1+ZsLlvbBb8ms2Zna0mR3GEMcyMVhTNuoqGsjqsoOVuIBcItsYtPT5jhyyvGrn70Lr/3XW/F/xieMEia2VHET3/tecoz65Pv1qlRB2dJgHfTiM0pF4TiIHGDHYkXm1hLCbb5RsTJ4O5AXXCsjYXh6YQ6UB+E1icIBUxOmTK3Sjhhca+wpy3yOWaKEiRxJSLPSrHB5/qjgIGqMPmv7juL26vr3XAJOkmxP3ugkYQSLU0qYmOr6VPJe5FmLN0XeT1eA1WOn4QXbX2WCqwBQOfPMVHM2kdRkEgT41Rv+T2G/fx6YKwlz+4b7zOtH9j5a0vK5BfvY3bK5O7EWtq3nJQiidvmx57a6obfIelZAq9Cq6jlZpoRpPvigGZc2rFQqcKeC6Ztu6mhr17riBBiEA09dXmx8fD66DgA4PCPH4f+5Y/ucv1tGshuVKXXgr10LoDPQcFSwEohEu93zUO/hWQ29VhvopoTJIWF66KGHHnpIYy4OAoyLlBKGCl2PuhOaCLdjDgAAXRcmZ67SMX8R85do08MzFz0Spod5g1Zq6KznwOtLlDAqkx5CJFZB2r5MB7y9GiJHWxAFpl075oCq8WJ754hYkzDyO5V4AJt9H1ErCdoFOYvCPSOW1c2X34zIs1Q5FctKyXfQppZFVEsrePKVMH+wu2rsyGzVCms20koYailhRJAclxRUgfvAA1EFfpuVBWjcdVfH/jDO0U/yM8yz2b7cyi4kKrMTa18k/1a6H07VMTi82QRGCTL9IwKUKwsgy25Dt2+p9yb9xdgzQuAowiZLwkSWHdnYV7+auw8aMReYUgGOhmiAuI4596Ej8KFbPpBqrwPEAtwsbP7h4CG4loKH3X6v9QXZRte60bBJGO74WI8KosaMIR2rQVpt5YysA3fk+XBFH0Z5umZLioSpL8Cg0BZ2FFDnh+msDYuEGVDqHN0/uwi0UN5Tdk2Yk1Z2kgEDrYWIG01DwgRi0gSXWjFXiiiJVijPTXumadROYSCP95Dj4MF1ssZKq5LU45m6/nowKNWG00agyR6+DRFtg4Lia5cuMu3j/fsTEgYct24+hId2TWAhY6YmzOPHnYXRIbkvjTvugAAwU1uCFU9eiav/+m7c+aUlePcd/4L/c88ncP1LPoUta38hUcI05LmmtRpACNrqUuWNxjEL6gQlJMxclTA6G7IeuCbJpqwuTKg+Y0QkNWHK7MhY2o6sLMAY86QmjB6necnE9gCXahkiWFKzqr+wOfSY53kU0760Egz9AbA8JUwcm3FH1oRJj0U8TN/Dtnz8qg3vwvP3vBzR4TOx7GMfAwA4AwPp5q1kXBqvE7TmMbZ4JLADMlmLyTxMTycWd6vGTzkmfXomwl6IDdcKVKgWoow9X6nyCwCLrftnFsqZHp7+0GNwzS/PUAeAgde82ij0JqqqJgxx0N64qaOtsOY+nAIeSVb0tr3lzxPZPSt7doBbNWEcdWz27Zu/vtjPRSF6lmQ9PKuhEwT61HqiiITR90XoHbtEoh566KGHZzriOdTSjDKuEBRlRLh2Hsm8X6KEyb6Xl1zYw3MPPRKmh3kBGx9PiAbF8AZeNakJowp7g3OLRU7qpAAA1rzQWBAxx8dks41P3bBZEQlKCWPHry+6UP2eWgxSmRHfbsvfFOAdShi57USJ0xr3EMdhZ18AVH0H01YdjcZ9DwEAwlOTIHKgasJUPAcrm30Y4coH3K4J02qCq46ftuAMeA4F1/UuRFEgSLaPfQoeyYJfM30rclvGXMBFfiD3p4+nPb659SCimoQhBKIyBKLqwjCt4uEM0DVYSPphFDu+USClSBgdgKBS+fSjNR/A0IwwxIEXB6ntRMpmgnBprsVGvggAaA10WmfEjMNR/ZnkM4DjgKq6Ey73cNf+tI9xUnc7xqAKuC2PGUZOkYGC2Kshrif9GV+lX6etL/wwIbgY9fAzr46oOWMUHfoaMHj4G2gFSgnD6ognd+BArAguwuDbhWwveDd86OvRA/71XCBuA1PyvOlrGwBcItR78l/vyT3mM5NUYSlhqF0oTmHt2BlouYGxTPFOON+ouL7/0J6U7VwYyT5HM21jCdYK5DXiLrkUt50iA+U2CbP7d38PbVUkdMYbh6++t7Y9hvHqAQDAptWJcmbmtttN5nCs9vWlJy9ClQujhBGV8/Dg8/4/XP+ST+E7E6/AzS/6OO56wZ9haPcZOLxrGo2J9Exox6rLk6C7UsLQuiSR2hZfNmmpauYToqz4vEXCaKWfbRGYhW2FU1GKwVaJRYxtR6ZrwrCSyWc7YikbPF5idca4MRs0ShheEpCMmLxvCGdgipw+OFjGwsgOM7eGtqsmqISC5SphmLlu2lOnJ0oYNRaJVj4JA0vZeKi9EKQSmO2ltm8RPzsXE8RudrbdCcEYmg8+eEysc6I5LCYAYHomCfCumjh13vszV+z9sz/Hrt/9vac8m90mYcoIR42sPV+ZUgzovH96aphnPrQKraqe83GJVRit1sy4pOeygjq5xevHvnq1ec0J4FnLMT5ZZtt47KD7rlGqhNFKFcdFuH0bAKD9+OPz1pf2k0+m/h77n/+Zt2330MPTDVk7spDx3OclO6ySn6o9JUwPPfTQQxHiOSphHGt9SEAw0SxI/MixI1sXWr45eeuczLzxwP/7f6X96eG5gR4J08O8oHHvvUYhEtZUcNoJEhJG14lgvEMJY5MBkbIji9wahAjxD9duVHVNtBImGfV09nKlNaq+L7cXhTrgzeG7nSTHApbUpDn0SD8YY1Zfklui5jlY0RoAUYqP1pe/Lbe/OMlA0nZkgUuxmIyBUBmYblSTOhm8ldS96Kv0wXUIoBUzhuTJV8IsG1qJdisJcpOcIqicC3gi/2GxeLiJj935MWyb2AYAYFbAl1qZl4S6IMqOLFJKBhBqaryQzEPlZ/2vtpQwKRGn3Bt1GFllGCsPA64iYfraaVJM19nRwX9HqXFikg4GAJJs0sXlY1V7x+GahOkk27T/v62EAYAFFWk1FHl1MIt00OeCOeljaZMw3PHhxQQHpg6YczpQzQSWh9dh54C87j0xgHPGd+EQ0zZ16Zow8OsYgCQnYq8f4eH9wO77Udn3sN4L03R0kbYXU4H5PYfNZ0JoJYz1oNd1mCyMNJehESbnvbJoBCcvTWrxXHv635nXmoRpT2miLML5y88GACw67lUYq8lrvR0MpS3G1L3edptw1bWxNI4xUZGWdA5JSMyD//zP5jhCkUwnLx1AmxCjhMmCudXc9zW4k9QHYmOyj86grH8UWsPBnj/+YOl2jhisOFBrB/ur55wj3yvJ8I3UNexSirpSQpUVGDR2ZCS5cuKSbOZ2xl6prJi4nSmkx+kyEsZXZBQRDNMV2YcGrxUG4o3C0HUQ04TIiKZzCkqz2BCJYTyQsrEEpH2N2a71e7UoGUufpB6IyuAWcfoc6Hpj43OIc0xecw22/cqbseGss+ediLFl9a0uiwkAeGTPY/P6+0cDIQTGv/51TF13HcKtW5/SvrQtEqXbogzoVMJk75cssvdDj4R55iOegx0ZOO8gYThxUkS32e7Bg+a1oASuNR+bufvuo+637M7crj87oQbIKLsy0MkGxHXRfPChuXeuBEKIFBEOANO33Tavv9FDD08n6GdTv7VWyVPdaZVuO+iRMD300EMPRYhSNSDL5+6xlWQIyEjW1kMzuW3z7MhCQpIcv1mQMI077yztTw/PDfRImB7mB4SYYOr4akmOBG6Alq4JY+zIbCVMxo4MwGIqfdojfwBcEQ4b90+B6EL1TjojOXaBSnscQBKAayolDCMxKk56Uclf9x9wNAFCHfQtayEMIxPQo9b2XYciiIaMskf317YY0fU0Kp6D59EtEJ5UJzTdJDDPLBLGdQlcSo1igaOTbHj80KNG1kjrNewZtPzEczIqYy5w9p78WgvN4f/C1Ruvxq/96Nfk1yd2yX0ATx13NA6ZmjAmfEkdCGUDRzJKmNOrhw0JM1217cjkNrnqpi5mSxXx5Yg0WRIrwkwf41DVDuKik2ySNSlUgMNlmP7pz4wd2fN3X9HRXgfZOIlNTRgsPAkVOq22UQf5xo9Me66s4QgyJEyU2JEJ4qASCmmjpq6Hup9W9+DKv8N4VRKDrWAYlVEKIrTlVsaODMAAZoxd2wwfBvw6pvqUpZVFri3/5dfJ/pkHfHL+NAljK2FQGcKSw5bdmsK+PVqhEMIPqjh3zQiuPEOqU/7f/VbGvVLvRNPyXzeeQUVZIJyxbDEaniKnCEWrssB8j1F5PLiTBKJnzvgdzPiS/KpFaesnfW9E6hp40QkLUT3uZZjxkuPuWecAABYeuBW3nP8JvO2vL8Kv/c1F+LeL/j/897kfURtMiN6J70jiVGfVMqe7omGuyFqMibh4sjd9/fXmNXHzCQAbOgDoOgQD6hqebJW013ZkAGJFapWRMDMzaUKnzD4pZslope3IykibwdiqEaauSwJaEvxOiHBOmMk2Gv12Z2F5wWKjNmTCMcqZWD1nbDuy9saN5vW6sQvM6yXNARBPnYMwfUw1iRN1DkNyP3JIlqmf/MS8btz/QP4XjxBRyo6suxImiMuJyp8rbFuheSw4fiSwrdxmcxyz906rGwmTCVqXkZRsegY7fuM3MHb11YVtenjqERo7MvWcKlELCsHNXNIoYXIIGACoX/AC87oS1THQSBSimCfrrZBxLIkJ3joV4MSQdr3mjRWt/rtkX/V9TVynNPHgSJCbmFDyXO2hh2c6skoYoGCsUfeatgjWsC2wjxa6xlMPPfTQwzMVc1HC2DVPAaDKouKER9uOTH0nJDBxu47yL716dj0UoEfC9DA/IMSQJlBBZo96iJyk/gYAGYQpsiMD8M6GFWxVBeJ/8uh+mILsGc7ip6/sS2pkKBJmWhV754TBd9NfoKsvhGvsnxwc3tAPHicBNZoJ0nou7SBhTK0RwYyVU+BRXB2/BBt9NQin7MjaZoLsOg48h1i1OxwIkNQgfdee26FvTbfeB+GpuhY8QuOOTvacc4HlE6Md78uOSXJmrC0VAYnPdueCllFtn2HXqdET+/RDZPXBG3FgQG5r34hNdDlqG/Iv39F1TBQJk1GsxLEuQK/q+2iSiXcGy2POjRImdGLwdhtTfavM5yRj0DnOdH0ejoGqOh+/9DlUqCQPIq+O4OaEpBCaICLpAIAXNVJ/BxEQziTXqetlhtGBlZgOkt/gLcf0W5DkmtHY9bLPIwglQTHJRgDHR8T09ZOcp8F6gJWXHIZWwggrc1aTX4JaEw2/jsH2NmSxZZdSH/EWXBW0fuEJCwEATQQm+B2qcyNmJGlFWQuOuscGqp5RrgDAvRf8onltiqPTJBBeXfUSNDQJEw5CLEwUCXrc4OoaO3VZP161/IUQQeIvf9Kmb+CyG38bF971UZy88cs44/Gv4ZWLhzGwsIr+kUzAmVAzxkxeo0i2krorR4tsTZcyO7Jw507zWpMwZX3TWdeeQ9GvvMJnSpQwsQoCc3DExo6sePKZzfQXJXGuKBYgpiaMsiMrCao76h4mgoEqEsZjoiQQqNo7Dihg7A7b+w92NrXsyGI4RnEZKZLQJlX2/smHzWuPJ4SpFy6Eo2wK4717U5vXSpYiEqa+qbPGw9R1P7W+n6/iOlLYwZjZkAeDGEn9/VQuArJWb08l2lYgdzZKmDCrhGnNzY6sjKSc+NY3MXP7Hdj3Fx/t2o8enjp02pGVKWGEIYdDQ8LkDyL6nnxkDcHb7/1rnPPQ+7Fn2Qvnq9sAZN9f2PKwnFH8YiPAZJG9hgLLKmFKnh3GRtN1QftLi33NGbaS0bz3FBO4PfRwLNEySphk7ZhXk0Co8ajt9qXe1/Pu+YB+Zg/+4i/O2zZ76KGHHn6emIuNc8zTKdFLGuNd7ciC6l/j3Xf+C4YaS9AmdvwpM0fszV16KECPhOlhfkCICYpRmgxAsacK0CsSRnBhJngddmQAYs819QNCX2bM37TpIOwsaRu7Ky1DwjCnAgCYbsoAOSccblY5Ul9o6qcYEmh0m/nYJoQAYIQ2oAdUHehOYkrc2JFVXAcPiePhqwC+sCy6eLtliri7HoHrUPSRRObIHD81ZrM4BFTfnHofDq+oqP1xMPa5zyGLmAs0g+5FhgGYoulA50NhlSpYL6iPRtQAeAyh2RSSPi7b17wekzUdDLaVMIqoUjVwPFVLweHyHDkiSAUEmc6mVA81IxLpqHgmA9JUKUrOXnQiaKWSFPwG4MdVcJHsV0URH1RwYyWCgeUIiCRIwmAIjepi9XsCk7Sq9iEdACCZB+qV9wk4m68zf2eJO/QtxnmBttiiaDt9GFHd4iTuIGFqlWH4bXnNjokRQLCE6LPsyAarHvqXt40Sxs6wTc6TdV4JQbRiGbLYs4+rpk3UAxk8ec3ZywEADQTmXLRDGWgfGX9CtW/BcWUQ21P3lS6gHtHjzPb1YlBYllILh9YYZctAawHYBWclfVfXuiAC33j3RVjQF8B1fLxttJNsqDUPYMXe20EFR2vx+eb9Cklf/0+8+JJU0IZ4yev1a5Pzdfiznz36AHWWRCnJ2A3WrUv6ZJQwJaSNVsJQYq6bssLssVq0f8D7Kk4kkoAt8/V3mxmrr5IAYxQm/TRZ3mWZ0uojwhkGJ+W4uHA0Kuy/CVYaEkZeP1mLHPm7sXk/FpbtpXrO9L3oRUk3LEKkGg2a17VwAYijngXZmjCahOkUKsrtr384/wP9mzlBxKOBbUfWLiuWrbDEXZ76O34KiZAyZdjPG+05KoqyQeh2WE7mssz9M1s7qHDbtlm16+HnD61C03MIxkXxM8OyIwvdpCZMLtQmGPVA1Lxpw8lvnadeS0RMYGWczBMKgwoK3MnakZWQMGpMIY6Lhe9+11H0MmfbOeMnrVTm9Td66OHpBE241HwXehka5s2vVLHLOKOE0U4Q8wE9/6mcfjoAwFu9et623UMPPfTw84CdvJZHaNtgXMCx4k4ExfMlwTna/gAoHQIAnLX3UkSEmMSUjvlLDglDgvkbr3t45qJHwvQwPyAEkcrMoV4S3JhyZWA/diqSxOAMQnna5tmREdeDXp2uP+fDZqEqVJCVeumAd6UZwY1VQXnHBwTBt1TQmNMYHs1c4n49pYQBgNi6DbIB9elIoOWrAbSusqxNYIUjcBMlTAwHp4WNVH8BgLdDs6+uS+FSglXkQPJ5JsgYjyfWYm5fP4YGVIF1QnOtLRgX2LFooOP9PLQ9Xay986FQg1IQ0Qoa0YxKi9fB/XTbsZUvR1XIiXqdWItjou3I5Pd8h2JgTcMoYTzmg1nESazeR4aEIbxzP2Oe2JH11fpAggAr9txiPq/Edfxwyw/N31w9UB0ARJNIXtUoYQDgzgv+XDWOMepIEqYtuhfFjePknHWQMITgz959PdxIKkjadMAoYQZ4Z+Bv+VDdWJ6Ni2GAM8vGJml/3KI63hX+PhLGzvpdRcIIkt7+Mj8plFtpyRoyQ8pyhIg2ltakBcpAxcVLT16EaVQNCXN4i6wrwSJq2ruZhd6t674FABj1k/o0ejEYW6qcpf0L8fy2bDPcXIKonmxH34ccSGr39C3B0spDVpv09fDll1Acbvk4ONXGloPTuP0VX8QgTwdIbRuqfSuS9z/++mRbBz7+/zB5zTU4GmRJFO3ZnQd3mUWKaSusqLsSxnWoycRulgSPddDs3prAGa48f2V2ZKv2bkm/UWZHZm0nUgRbjPHC9nqIoYIhCJMxtzj4rYl2BzEBWp6uOeV1HFNh1YSJWdXYXs6oel3ET7JCRTshAytxMk5Woz40FfmZ1Y/zLkoY0iWziU9Nl34+V4SWzVV7NqRGO93xqAt5cCzBZ5KEg6daFWOTMDEXKbuCPMzZjixbE6ZECeOMJBaOO9/z3tLt9vDUQfuKazsyIL9WAwBAJCRMTBSJXGQTpJ6zzBnq+Gjwta85wt6mEcY85XNeZmUJABhZlPqztCaMstEkrgtnRCrvSHV+bBDzSJho16552XYPPTwdoZ9NgUuNZXGZEoY5+STMfKhe+bScvziDar70NFKz9tBDDz3MBnb9vtnYkXlWEq8gFM2idRMXCP0koa/pTUsljFoX7vmTP0m3zxmT9Zyph+c2eiRMD/MCQgi4yoB3XGvgU8ExEIrYrQKc4/BnPycJmRwljHAcOCIJcB/XR1TRdm1Hlg54P74OcFjL/IbLPdxfUaobwuDk1IDQAXkd2I15ku6crTmzfLAKrtQFQgUS+cy4+pSbIusV10EoXKxmTbXtZOkrwsiqCUPhORQ+OGKSBBntQZpZbLxbr+HXT3htsq2cBT0TAmcQWfBYJz2O1QHauetgxuYr80B6yYfgQZFZtIK/uOkDAGfon5a/52VqIPRHBzGiCBRqBW51aTOmlBK+SzF8fMOolQjxU5YtXHl/GyWMcT/LI2E4oASjNHBBHAcLDj9iPq/EdVy77dqOfRW2ksWtwqfpgO4D33sXELdAlR0Zh9yvFZ/8hGlzwmZJNniKWGEWCUNyDrTv+CCqnkvEhpGEQnLIr4qHQNU1+nqwDuAxEq4mmQQEroOdiy8HUyTnzKB1LeiaSTT9sD9rMKl54yiyMqYqYMJbCJSyhRCC//r1F+C4JUPQJM+D629Am7XRZqqNaMENEguEF4/8plGtEUvIq33lYydZuNV8B+H0yXI/WA2jr77Eaq+uGQhzP+GkV6LfSYjKr16aJipHpgTGGsCFf/szXPb/bsKPHj6ID4yOpdpM33Kreb25mhyrVpA+X3ve/4c4GmQDy/GBAwUtYUiO4LRTZ6WEidWkznOIycQuI2F2HpLj7fQuD9+vKjIsLG7Ps/dZyfo9UgFpDoaYqvu2hIzQoiwiGIjQdbhctApIBG2vR31V64Xq73hoPZxWnti1N2JBQZmq10Dl9Tl9a0LO8kZiJ1gPh81rAopJZbWXnSTrTNDYUsKIi8+z9i2jnMkch9ajj+bu45EibUc2C2l7hoQJSyzsjjXGv5bUPBn70pe6thdCgE11J8KPBNmgVreFWVYJkLUny4Jn2pfZ9ZEgIQrDrVtLt9vDU4fEjiwZDOKC88qZgFBJNTNBYkfmLl6c01gFU92hjo8m/vd7R9Nlg4hxeFayRlclTKZGYZkSRis+ieuAKNvZ+QrWahLctjkLt2+fl2330MPTETq5ouI58NX6qUgJIwDE/oL02zqZ7yitbwTnJnGCDkgSRoju2xRRZO7bHnrooYenGpE1FnZLXou5gGetAwVxitcHYRNtP0noC+IquOUW03pofap5HjE++Jr5SbTp4ZmNHgnTw/yAUhNMtS29OGUIVdA78uqY+N73MHPXnal6FikLMOogtmpJvPWMIRw/IEyNFUrSGXLNCvDnb+FmkuizirGsEoR1KmEAhMGg6ptsN2mpVrJ2ZMytQmgShgsIznUCIwhYRgnjgmqrM9uOLI6TmjAuhesQeCKx9ImdtM2CPVy79ToW1JPAIaed/jiMCZymSRi1Fg4EQV+Q46XD9W9kHi4LToBHFIHkBLjpwL0Aj7Fyv9xgZSpdGL2/vQ8koyiSr+XxnnRlEM1zKECSmjAA0LbqNTCWIWGoriORQ8Kw5DpwPAdwHBAA/VNycR7EdcxEls2bJibsI0op4KUzyN4+ehsQt0ERqCZyv3xLgr/okFRlMEUwCO1TJOJEZZNB5Mvz1pp5C5LaOjkTgckJ1Jqy7sXJewbBeAzONFGYbj/ZjtFYIN8LLe9oYRRL6fPqnPer5nXTyyg0RBtBRtny1gvWIAjluX7VvQvw7Q1fB9HZILwF3yrce8mS1+Mtk+PyZ618W52Rx2nS93rgIqYcsaoRNTZtWalZSpjVI1LJAErBLCXNeH/6Wp6uAvvHE/ufx8YcBNaESwA4+I//aP7eujT/HM0Hol27U3+LHLWTgfqMEKpUf8gvQqy3rZUwlBoSplUSCD40Kc/xeZtjXPyoGl/CkgV05j4jZXZkivxhNDbnNu8+1aCKKCGcweGKkPTqJSSCUnMpmbZApH7P67BDtIlqRoipv8CIzMQONz9pyBf9rwBQD4dS22lPKMKsg4RR/bVI+fDKxOIsG2ycvumm9HY3by7YxyNDioSZhRKGZEiYdnt+Cn0fCeKxhBxtPvhg1/ZPXvEKbDr/BTj4iU90bTtXZEmYbpZkWTuyuEQZAHQqX0SZHVkvu/gZgawdGVCshLHjn5GraqpR11jZ2tDXBvOGU+/zHLXzkSIbxG2WPQsAsEyST9n1a5IHXBdQc+n5Urrp8ddWNPbQw7MZel4UuBSBGmvyrEcF4+DUM7U7G55caxkS5ijvQTtpxRnQSpguyt8wxIYzz8LGs85+ytWuPfTQQw9AWgnTLXmNcQ43RcLQwhp6Io5MuQQAqMR9ue0MrInh4GtlUrVJXOnhOY0eCdPDPIGYgqRuhshI/PprGPvKVxFt35EK3NtKgkvJgMmuB4DXHj+In/7BJdAKiOy4VRECm1YRELVwXTS9ylhWxYTDyVEpnKqypXUfrq2pbDvBOgLqNyz7zYSEIRQiDMHNbZPUhAlcBy5iQ8LoYwHIxaqpP+NQeJTCFwKhK0mP2KulgoDcylp263V4ftq6qbItnREYcwGuFvmahKk0BforObUUtBKGZEmY4+EpO7LIG9KNDXlEMplQZPWFCQmTMrzQJMw4AGlHtkMsAuXJw6zdskkYVZ9EWZT1H1JF40XnAyrmNglDMfzmNwMAPEW8VKKaUXbYfbELyAMAgvQD02M1IG4hEPK7y/pkPQVboeDGKqDr+ODEtUiY7gsOwZeb66GZW9+CwY3k9ocaNbTDSVPQOUvCvOzUJYi19Zh13PVVS0hm0tCf2F8FUcbiQzRRcdME4NtfuBYDk5LQ2776Cux5/Efo261sxKZbqHqJ3chQzce94fMBAL6wSRilKHKSa8ZzKFY44+DBHgDA4SceRP8VV6h9VNeY46RI0AFr4Ue5i5vOSD77wQsowig5locxAPfUX0QRorPTNilFNlNHgn1/9VfpN0oWrEYx4TggniJh4uIAubZL8lyCyizsyLQQinCG4Wm53bLsfV1LyInlvZ+9VVJ90UoYwsDVGE1L2juxIhIpQ6U1KvviD+QGvuVxUfdrLU3CcOp1KE1sBQ8TFLEr+9N0kpo7W171arCpKYimGmfdKii0YkbeC+17r1a/lcbMbbcBAPpalqrTIpK9iYlU++Z996U3MM+BiLQSpnzbQgiQOH2BR7NQwuzfOoldG8e6ttM4vHsa7eYsFDZ2PZut27o2j3buBAAc+vRnZt2X2SLMnJe5KmGiLsd+LnZkT6daOT0UI1HCJGNOkY2dzVnYyUS5ZIZWwmTsyGK3doQ97USWdMxe/x1dQnrcKKtpZNeEIbr24nyRMFoh7c2u1uHTEWxyEuPf/S7Y9PxaU/bw7ITO1PZdiopaV+bO9TiTrhKQyXQtV6nz1doiO1eaK7QVGRwHtKYtuMvv6/GrE7VrT7HWQw89PB0Qz0EJE8bcqJgBGe+7dfOh3LY8DlMkTDWaPQlj2b2Uf6eH5wR6JEwP8wNCjPrDthd7tXeSIWEiS4Fg13iwA6/V6QMIkAS7Rm+6W8qvdfB9Qbrg8N96a1J/X/7Er4GqeiKM8A5CCACqg0vk5yqrf6BRbBXVDBYldmSgEAe3QYgkc1/bJzmUYLEzBUp14NDK3GfMFGelDoHrEPhCoO1ocqoKbk1yRcsK9tXr8P0awJPtrv5MOjjFhDAcjrYjowLAxBjO3Mrx7h8yVNt6wE8IpBRqC42aYnLoTNWEJXV7MiTMwuddZSyGBGxVg7J4c+TveS7FfeIkEAhQZUnWbib757dHVX/l9kc2ySCgyzszIGPGAU30eQ76Lnmx/A1NwsR1XLbqsqQvRhGV2VAzHWishQNA1IJQShhfLYDsiIobt4yHe+TVIIxPUfGCZ8Xum1T/pgHVlzbt3C8RRQnJQ2toPvzNRLGUIcvWLayjCaXGSWXvy+07WRImTJRBC8f2Zn44rWzRqDX2AwDGh09COL7LEEiEt1F1EzLjRScuRCQ0Sedh4ZlSQRNWVBDdSU8yPh++HITKBV401cSyv/4rtc/KlstJq10WWBMXRzi467IIC1+8AJ/6ZaAZEIxOJe037p/Clr6rrG+nT7rgktw7n8nvfPYV8/foY6Oj6TfKFsHqnBFCjB0ZSu3I1H1kKWHKSJhFynaNihiusmlsN8oC5fI4ubMgYZgKKHPCwYlWwhQrjLxQZXq73NgRcurlkzDtdmJHVpXE4Eyg77d6xzHSShiOCICDwOISxwePl9/bswejX/yieT9ShWyJJ9BWSr0202RqescnvvMdAMC6/cl77ShRkvn7M5Zzmez1+PBhzCdCK5i/f7KzXoINW73RUjXZ1u99uKi5/E7M8c2/vxf/+08PoDXTXTVzzw+34uq/uhuf/YObu7b1V69K/ii51n8emLMSJqN82Te9t6Clap8JzpcFsdElsNXD3HBwxxQev33PrGoizLRjfPeB3Xhif3fbO10TJnAdY/EaF5zXxO4ViJxknpNrS6fmE8JJJwjojPaijPJmGOOnD+zA9CyI1ShzPeZl1ttgmSSRchJREyVuKjvqaIPAACAieeyeyUqY3b//+9j7wQ9h6y/90lPdlR6eAdBjimtZz7bz5kqMG7V55ITGGna+lDCGAA0C6RwAlNYKBABnMKmPsOWqXziq3++hhx56KMNs617ZiuWICeOckYcwMzcShGLZYH6NOxa30aglFrMrJk8q7YfdX6JVw/MwT+rhmY8eCdPDvIAdPpBYblkWYIFTQcuTgaDIyvCzSZhUHZbx7YAVSG7c/R2gPZmQMJnCnydflbYs2d+/zdSUYTTssBcDgL6TXiA/V6qJS3RZkRxVw7lrhiF0ZJIQhF/5XQgqI34CwtiRyd8L4Gg7MlsJwxIygzoEnkMxKfoTcsqtYSJMMquJVfzXrVbgOBWjFMmtCcMFvO1ysTqUKMnxK9Vx/OnVHJetF3jlvWofTJ2UzAPAq2ISVtFwAexvHkxUCpljQxzPKGFgZU9qQoart3yHYHpQnncdiG089oRpv3BSHXwVkPAg21Du47MPfzb1mzEX4Mo6zvEds/C3SRiaCoYWKGGOe2nqz1rUD8RNgMjrQcfGK6edmuwvhCFKYrcGaCVMnr2Ygq9svSrNQ+Z6EDnXowhDeGrbkV9DK+gDV2quLAnzwuMXIFbHuyE4GkpBo73cw4zKB34fLiP/gOV7bsHa7T9Kf4Z2rpXa85f9IOnbxIlo12XweuOKNiqWdV5f4GJlRVmuCAcfvryKU2/6JqaXKiVMxg3vnFNOAFTWnhCDcAYGsORP/gR7l2nFROYLAyvMSypcNFyCRRcP4c7j5XndvC85Nut3TeDTN2zr2BeNUBG7V/ZJpcTmZcl+k9rRZR5nswTLJlcmsEZpooQptSPTtZKsmjAlyhYXiQWYvi+aZUF1pShJSJgSOzJmK2FkH2j2JFsgeqyhHI89T143jPq5snARhslYUwnwjtqpWNiU5+rxU9/eUTdHBzs5iTFUCxAMJ58/ccIvJ9ttJ4HQyFP3RoUhVvZl4YqLVcPi/V6sfjuKG4Vtsgi3bk3ZehwtsuTBVEmBbdt+ThP9B6byM7o0pscSYqc51d3X/e7vz76GCa3XuzcqABsfP+Lv5mHONWEy9+Z4a7SgpcRc7Mh6Spj5xdf/5h5c/z8bsOvx7mquT167CQ/+5+P4l7+5s+uCXtuROVyYuW2W3NDQ51uAg9Hk2skjM/RzQpC0GlUrSbf9yptzf+OON/4aVrz5FfjI524s7XdeP3NrTNi/nakJU2qnZ2rCuCC27e88qGGMHZnnYegNv9yl9dMTM7ffAQBS+T8PxdJ7ePZCWIl0DiGoaOvZvGcEZ2BqHh46LfAMCXO0wT0RqTqP9n3d7Z52i+eBPfTQQw/zhakbbsCGU0/D+Le/07VtVrGcnf+nPmtn1vHEKVTP8ChCKxhJvVdvDxV3xH7+a9XwMZgSPHTwIbzvxvdh9/Tu7o17eFqgR8L0cEQIt23D7ve9D1M33ggAOPCP/2wy2h1rQVbxqgVKmCRwT+1AcG0BYsuCadKpQNz3P0lNGC8z2asvBADctUoGjmf8cXhMLmJ1zZUsRkak/ZieyPa1iuWBg1UPsWVHxg/uMlY4AjwpJA7A91wQ0knCgHHLjozAcwh+FF+EtrYjc2upRZoIk4Ci4ziA4wG6PoLKgIoPyUxrISS7X9uSru0BADWrJszQjEAzbiaykCwx4fhwkWR6O8LFp5/8Fg4Nyv1rLF/asf28mjA6IzxWPkW+S9F2PRBHmLowE1//rmk+6S1V3VHBZqX48biPT9z3ydTveaObIZRyw/cdQyC4sQw2B3EdH73jo8kXtGVRdpRj6euiFg4CURMC8nrwPVWXJuN956lAdeTW4IzpDM3ixcnQuKwLwdyqsQ7LK6HhLlxoguChV0NICGiofjtDwpywuA9tE9wh+Nh33yi3q4mwbA0kx8PxtbtwyqarEdFm6iOCTI0YhYH6uHk9uOUKMMjFHadtODRT8HvoRPkz3MUd1SpE1IRQ6gLupAmewaqHUaJUPKrNyFvfjNFhuc16LZN1cuYvY4G7DQCwdeQhPBYEmN5+K9pqHwWbPXnC1HF06zJ7ZdL6auXMM2a9nVxkA1Vli2Dd1qEygxjJojcP2tPWcyiqyo6sLHufqN+mIoarSJhSZYPuDleFkMuUMLEeBzkYjdX33OJsf1OTiePACfLccscD37Wjs2kYmrHD833019J1ErIBa20JyUmMmufB8ZNrzYss+xfrEtTPHx5EYJ7c37ZQF0JBkGy0DzhB14eJmrltihDu6NzPI0U2mHpwqlgNEymSjpEYsSP7HqBS2B4AwlZyDc7GYqygDFYueCvpa/3SS2b/RQC8Obdj3g1Z0qW7EiaTHcfKd5xnlTBlSgL21KqCnq0YP9Cd/BzfOolFnOLUtlNKagPy3lsWE+Dbu3BJU1ndFpzXZBhJiGqgQPSkxk1Os0oY+YxsPZyvXlu24QH54vrrSvsNdF7vXZUwmclSVKJcM8S4cyyUMElNmL7LLwcAVM4886i3+1SBT3VXXPXw3IWdoe3QhITJq+EkGEeslTC0jQWqdiJ35Dx95s47j6ovRuFm1Xrqlgwx38kSPfTQQw952PWe9wIA9v7Jn3Rtm1Usl83322F6riMIxUM7J3LbsjhMxTMBoB4O5rYFkIoJEO34cQyUML96za/iuu3X4YM3f3Det93DsUGPhOnhiLDzPe/F5DU/wq53vwftLVshoshksrsWMdHn19AyNWE67cgEeKomDF7xt5h0kqDhoYkhsP0bTIDOzRabrwziTZNTpr6Kz6pwuZZq5weq/KpaSKuJLNWWSjkB9YGqB050fQ4KAc8QGcKqCQPIwq3EUpjogsQiZkYlRB0K16GYFAMpEsZGn0gsIQgBQB0IFTDXJMyhj38cQLlKvI20F/V/P/rfgLJqyyos4AZwkRwvl/mY3HkX2mr/eH9nNjMhnTVh9HllrjxGnkMxgimc+Iv7jBKmZXV60l0gt6WUNn2b9pnPPJYmliIWGRLGqyaf2TVh0tD7mnl7y42pP2tRP+LGGKAyUn0roLvq3//NvNZ1W2KvBqrrkZTUhDHqFrcGoTNM8+zxzj03qclBKmje8xUEU/KYLhzNECeEmMw3EB/fayj/ZU2EeZkh3auCKFuwWgjcufobybZEfoCTvvpvsHqHCvCwRWh4KwEAnHYGt848RwZVdR2mdnMUQiglTIYw9RwKru4P0W4BjVEgnDLHZsnIQKo92lN4w4I/wqXL34mJ6kEAwC2KqOljHODp68O+orW1FTwPJ/3oq7hJfa+x5gIAwEQfwa2nyTYHW+UqgW7IBlPDrVuLM19VNI6QWSph1ETNtRbmjZKgoaN9+jmDr8iIcBYkDGWyjQNS2HdtzcQJx4wvJ6d15hdObBOrMo7Bijz+jPqIcxbsot02xatdz0clo+gKt25J/a2JcE5iVFwfDvEwEcjzqGsaAUhlaOtJswhiRJ68llvmPk7vs3+RVEt+7RKKAXUOosi6/rPHyE2Tk/ONLAkz2Soh7tT1EdPQ1Fcb9kYK2wNA1ErOYVhqXyehn6GzgWhZ48xcM8KzpPJRIqsE6KaEieL0HELwbiRMpiZMyQO6vWGjeU2CziSKHmYPmyyjTneG0LdO+/rN5eqmiAm8rOkDAjhHWdcW25HJfwW4VFCr5JLpu+7ubKztyGhGCZNTNy4P3vDtePDAg6VtshYb3ZQwWbJouj2T3xDJc4+4bjphZR4sB0Wo7Mg8b/bZ+E9jTKtktR56yAOznovUmuvlzq04A1dqOUFDuHUZ/NNKmN2/+3tm7XlkndG1npyk1hOA9pYtRd/A/r/66yP/vR566KGHY4AOO9aS+X67QwnjFs6XGIs6SBiXF8/bbCcHotaKZev+o8Wu6V3HbNs9zC96JEwPRwR/9WrzestVV2HwTa9L7MisNfBw3bLd8nLsyLJkQNCPqUoSFK006rh1523mbydbbN7x8OHDY3jXpAze+6xigvcRLSBhApXdo5Qwul6LEJ0D7vGL+ox1l4AkYRIljIBvTVLDygJQnhA209ffID+wlDCOQxC4FIz7xq6CUzcVnBqByowUXKo9qIeJelJcGgAiVfwwLmHTHT8JLlABfH/TN0FQQMI4PqiVuenxABEPoa3GaE4wzPRYEwBCmP1UXA98h+JX3Z/B8YTJtmdWEXcdpNI1YahgJnDos0xwwnLJ8frlwiNcXDckzIqxOs59gltBZEWu5VmAWSH7wdYiNEf3GluQILBIxEsvNa9di1ThRv1TfPxt+zKjFsqJ1RJCsO3yV5kM/km2LKmTIjqtgSjVhE2SeaGJMJJVih1/OZwg6SNzb0r6Fz2Y22/nzKtw3Nbvmb8jFcB13E7S5qWnyJpMVJEwrS3XgytyRGT6csFxI+a6E60Z4F/OBtrTGFeLRy/IHJxLPwiHxDiNJ+PBXRV5jgZiHx11X6y/j//xj3HSnXfglPUPwfGT6/qnu5IaFvedINtH7CgnQzkTtekbbsxtarKEHcdYOGSttmzojGvXoeivyPZlVlQDsVx8UxGb+yJqskJiRR8zfW8Cndn8SV8SJUxMlXIGBM2i2gRGCcPwUFMSI5x6COPOvvB2yzwTPM+H7/fj+hO+BAAYHN+MA//w8VR7PYZwEqPmexiuBHhygcwQj107szy5Jp5cLokdUYkRe0p5pm0FRTZ4zlR/BarqMzsg3zjxxFR7b3m6Vpn86TnIRbogyiweJprF10CoMrpiGhrFUliitgIySphZkDCsS0a9Dd5MFHeiMTdlC5vv2jpzVMJMtdO1f0o4dwCdNWREiRJm7CtfSdr1/KGPCjaJmGexmYWdef74t4uDi4BcyKeqr4nieZewSBggSS6Z/Nn1OW1V4wwJowOs3eD4o3jbj95W2iZrsZFXY0Ij2r+/kwAquyzVmPLE1BZsnU5Uf/OqhPE8k4DV2rDhqLf7VGHPB/74qe5CD09j2LeMQwgqKpExr/6fYNwQLpxGILoUjHXv7v3Inx5xX8x81HNTSRDzqeztoYcenr24Z989eN+N78O+mX3dGx8FulrJZubfRfZiADCTcQAQ1IFbMJdkcYRYkTDVfjnu5tUx1tBrIBIEmPrpzwAAo1/4QmnfAeDaR/fhP25+cs52pgTzt/bs4diiR8L0cESoveAFqb9HP/N5o4QJrIlbtTaMtqtrwnTakXWQAV4FL4uS4reRV8cOK6jmVTozRgmAtUzW3/DjCnxFwsROvre9p4KZzFUkjKkj0jnQVTyaBI4JxXTbSTyliIBrkTCbBl8MBHqQJ9j74Q/Ll5yboDp1KTxHkjmabMiSMEx78uqIj+Mh9Fpqn1QQ+rWv0ZsuxJCb7D8RwI7GflOnocOOjBBUSGKZ4DEfOjce6Kg7LT8RskWbuMBX3wwetU3DZf4L5XZciqvjl8htqMAmOz0pYqa7b9ec0aqK/vYIrtlyjXk/aieBR62I4v0DJti8cKqGP/4mRztUdUe0BVgO8XGA/rN5ffr+F6ExedjUhAmyaisFT9mexW4tIe6y16+Fne++TLahDmKqCMiCLN2BwT5UGzLgNxkvR6QC9OvXdF7Da5QFH0gVgb4AdD0NP9P3xaeAvvKvzJ9+JPDCxm/hpTf+NhxSoIQZHDSkmA3Hn+54r6oy4h3hgHIHrbv/A0LI40gyqpznrRpK7iXhAe1JNKbH4ai6Ig1ksvf6lwDvvRP07d83b31rQAbSZ0TargpIkzDOsiVwhoZkQK45bt5/x+nvSNqr5kft156ToTv1k5/kt1XBP0JJooQpI2HU+fUcggFFQJcF4IVW9XFmbPoEFwhb+ZPPEw5KMpdaDGeznd+WMa2EYSa4DwCNwqC9svUDx/HKBo47Hlo5JIxoNcw47PsevKAPbUden4J23sDaNoeRCBXPR91xDNlvKy7tMX3/sLx2vBqFcJXFWOxmWqlvqXNCCdBWE/E7yXbzOc8WjFYElTM0ZN5qPf54R7+PFKE69kM1eQ1MlpEwiqSLncg8Y27bcVtheyAdxG43upOSWZuuMoh2QsLM1V7s4Cc+2b3RHDDXmjBUK0f1k4qXT5k77Mi6FDQ2mAf1wHMZNok4m2POrMV5Y3e5fVk7TpMwp4cOopwxDLCfJTqpRM8dcyYhJmEnq4SZv2L0WdKlLAgRHzhgrNDMeyXqE13f7C/v+Rhe/wOr+Py81IRRShjfA9dKOs5Ln5U99PBMha2EcSgpt57lzBAugkYm69Guqdi4994j7ou+x4jrpWs99dBDDz10QTNu4jeu/Q1ct/06/N71v3dsf6yLmiRbE6Zsvj+j1lT2Wphw0bENAAjv2WSI8L5hOX/TZRDyoN0AaKWCaPfs67W864v34W+u2YC7tpartbOYTSJSD08P9J6wPRwZnM5Lh6s6KBXLjizoW2bsyCYG15n3C4kPFqEukoF1x9I18HSVd8FBs8EvBZ9qO7IKXK2EKbIjq6gJrq8y9ktUDb5DwS0S5rZmw/AlItP3gVoA6um2VuFvWwmjJswxHTCe4dkFOg91FqXOlPQQq4Bk+7jj5Vt1GVDUQdpdq+V215+YbGvxnhvM65N264KxWgnTGUQ42fmZee3yAJKvke2dPCWMISAcTDzx45TtzHBwCgB5/H7EpQVUS/FnzE8ytrS9C8kJ+p908Hz88S1JBmGsMrwpC+GqADZ1PKMgme5bie2rXoY9+7UUU2Xi51yrLboVZz+UBPi279gLqHollUoBCaNqQkRe3TpnxQGH8LjFhngK3X7Vl4LGCxag1tgPAJhxT0Vb7R/xO+uerOxXtl2kilVxugaRm2drc/HvAcpOrj4tszgIOmveaFBVqH7J/nvSH1Q7a8gENdc8RapRP1qEGJsw6qaP+3GL+rCC6MxyRWLd83lQRWpeuPKczs4sPhVY11lHYs3IEN738pPwp686zbxnkzApVVtrHP1qIrVmYA1+fXwyta3sfTxX5JE4bLrAA177vRAK4s7CjkwrYSjFYLU7CaMLK1PB4PAYXFkMtqbzCekVk/J82EqYZgFhw9UEloOjavWhUWCNRTQhRAR+YcVFAKCKHeYoYVqNpCaM58Pz+y2lYKfMm6naHIwyVDwXXt9SQ8LQ084y7exz4wp5D5y18nQI9WyIJ0d1w3R/lNWOQwSu6ZPf+9HAblROP11vOdXeWNJZ6q+9H/xQR7+PFKEKni6oyzGq7BpoK7I6oqE5htFclDCzqAkzF97SVsLMlYSZbxufuSphHGUxEKmEjtzaHhbmYkeWghA9NcxRwCaZZ0MQii62XDYmM8pDXxCEBUQDN3PDxBYSgKWctTuRtiNzfHnfNasLZ923boiyJExOjQkDQjvG2riEtNHBWkYB26VvXpUwjgN+YHfH+/MBIQT+/scb8JHvPlxKTs0HKmccZd25Hp7VsJV5lBBU3GISRjBm7lNBYxC1pmxWFpk2RxWE0ySM46RqPRVBPINtAnvooYf5xeFmol5/fHT+EtHywMPy+UBWCVM239dKGO1gAgCuyF9r7R96vnltK2E25RgiADLBBUgn6XWDTf7sGZ/buqmnhHnm4BlDwoyNjeFtb3sbBgcHMTg4iLe97W0Y71IM7h3veAcIIan/Lrzwwp9Ph5/lMMWlLHCVsRwsW2LeCyqDhgyJvH7zfqEdWXsqZYs10HThq0x5ymO4QQ4JUx2GT2TW91BrCTxdE6bIjkwF2SkCcEKtvnQGTAghCdlACKbi0GSbZ4mMgaoLYoJz1iDIuDk2VBECDoZRiWVwb/fyi1MxPW6CCJqEoWgFsiZGk6aLf+nJu0roxq6lyXnpm9pmXq89oHdR971jV+HSCDUlH/WYjxc3WtBDBM0hMsY91RfiYIfrgjWtjFJP1VdxKdrQC3tNBCU7q4N5eSTMoVo6Y4Ap2yPKQ7iuvA4GKkNGoQIATx7/OvzRtX+ggq+6WH1mZ89+M/paAsPjm81bdMvjpt5MUElfY0uUokmrmXasutwEVUSZHVngmwc6VyRMkV+9WwlQa8pz3GgNgaq+L6iu6mg7ogKxhFTBQBC3m4aEqdQKitVPyWP0ru8BXJ/8nHMKJHU01m7/kXnvxCe+AbfauW1CCOqD8n6rh4NoEQqilDC+30kITSlFkL6Hag993tRkqvnVjvZFGK734/cuPxG/dtEa856wL2prUSt23YeWugYCJ7DuUfX5UQaN6jnPlNq55+W2tW1WEiVM8URST8Rch1h2ZMVB8rYqwE64sqRS42JrOv87mkykPOlDkb1YS9VEWTjJ8PlPMHDIdq0iOzJNgoKjXk1qvGQD4UA6OO8HFXh+H2JFIOTVSOCGhIlR8xxUqkNoeUqpZteTsdiCGdWH/oEaDgqpujpM6x3tAIDvl/didugYesMvq/bp96eV3ZC/Zg2OBfRiYmGfvKeygWEbmoSx7ciWVVaUbt8OYrdn5pZt3k1Jxi1yfq4kzHxjrjVhPC7vJz2XKLLq0+ggYUrsyIITT0i/0QtmHTEia0ycjVVeNzLNxkxmfKMAWgW/oZNK9JxRK3zz1HzmuUPkc8+ry2tlpr509p3rgqzPOZ8qHjeIQ8Gc9FjLSpUwCQljWy/ymeI6MrOFUcLsug34SVJkdj4DvpsPTOMzNz6JL925A3c8Ob+2h9k5RU/B00MZbLLeoQQVpSLPHWc4T2rCODGwX64tdq94cdLmKBQsiRLGTVuqFlna9p5bPfTQg8LPU4UhwvwYn0bWNrZsvt9q6fhSBKLWw64A7t7WWV+rVVlgXntKtehyH1dfquJlA+n6tmxKJgo7C2efYNOcaeKvbv9P/OLmm3PXzGWgedY1PTwt8Yw5U295y1vw4IMP4sc//jF+/OMf48EHH8Tb3lbuhwwAr3zlK7F3717z3zXXXNP1Oz10R0ftCSTZ+H2nnWLe66suxOHanpy2alGaJT5WvQDUUhfEbh2uUsJQEcPJCeziNf8K37JWGm5IEqhICeNVkgVx5PVbqpz8yeQiptUGFKu2E5iC75lI3GDVM2SCIASVs2VGNuHCHBsdhA9oDQtnZMFzQT3YUT2h2HqbmIi8cQBASGqp39YkDBHa2iI/EJ1sXH7OcwgnQMDh8pi53Mc/LBiGJjKcHPIgDnRQ3sFvLVsMZgXbnKoMbnoORVukF/a2SkETArYd2bah+wAAFBQLrazQqC0X5g6LQJWKgDiOsSPTmJnYjcnpvabvHUqYC96FX76dg4rYFPE+xFZDmyvXMnWHBl/zagDA3mUym587gWWnVxxkC8Hgmbow8qFM3PwhN5iehB9Oqv2sQmijZ78zAF0d1OSXj0h42LlnvyGIBvsXdLS34TEYbk+ULNSmV/qoN/Zj7bZrsPDgA1i5+0Y4lXySRBfyvnjb6zFqWatUKwMdbQ+6KltPWc7FSOrJ+Dn7WgS/KuvUeNa5tc+EfW+O3f0ZRISACIHhyjDamQnK0Sphqmef1b2R/i29wPW8ZAwtye7VBaA9J1mYlxVX1vWqqNBjiPz+wZ35yhw99lHOTNZ2q8COrBHLiWRfS1mkKfKoWaiEUSQM4XBPPtlkfofTOzvaxo1ELRF4Hjyvailh0qSoYAxc3dsxjVH1HNS8wChh2u3kfB7+t39PvucoD98+D9OuHOf2UkXYWAGG+NAhkL0qc4kIvGtswt4r9f/0NTNz++3yWNx7X+6xOFroc76wXz7/ypQwUaiDozGYSmgoIwMAYMPOZB9nJsoXNll0U3uIVrI90Si3fjpSCC5mpTr5/kPpuUhZjQwAJsU/cuT1ybqSMJnga8lxdxctSv3dU8IcOZqWhd58K2HizDkMRLGtlyYkhbEjU5azJEddy4VM5lAkTN9KOT6NDiiVb19f53fmiCgTxBWTJWQA7VTClB7LyCJhLDx51S/MqY95MMkKrIm7qpWO9+cDj+1NFLFjjXyl6JEiaqStW+P9++d1+z08u2DbkVECVErsyATnKTsysji5J8w4U5BgNZtnjNBjm5u2yS6cJh/DAtM99NDDMws/TxWGTtYoQnbu1i5J0NFzHYfHcBQJ4wuByZz1ba0p14dt7wBcQ8J4CJUqsUPxotbWc7F33PN/P47zDmzEux75XvfGGfSUMM8cPCNImMcffxw//vGP8dnPfhYXXXQRLrroIvznf/4nfvCDH2Djxo2l3w2CAEuXLjX/jYyM/Jx6/exG/8te1vGeVnvUgiRYu7RvmQmOMbdiLIMK1SdDq0FJMujFXg2RCuITXkDCnPoqDHwk8cCth0MAikkYxwqEN6qLjKohT40BAAS6bgDFir0USU2YdLv+iodDGDDf0nZD4MIcG/3bFaeCR5beIt+Lm6kJrlAPA2KTQqoQNtPklUiTMK5qO96f7Bv3gUMLzsCWtb8AAR2QVeRBTrF6QgCHyWPmKUs3bRGUZ0fGzHlyME0p4may8PSULZbnEKOE0afaXnDwHDsyRlQWAvdQcZIFeKwWJJSHoEqJ1dqwwfRZY6gRgG271dSEgZvJQiUOagvk8Vx84H4AwMF2YmtVqaavMTowgPqll+C49v3mvdnUhLl+z03y3KrfBPIVRQCA0043tmpxXDMkDMm53n0XJqANXsWee39oruFaPT9w459zNgBgx0LgsM7KLeoLgBtfPwQAOG7bD3HWo58FAeAEldy2A4vk+0PNxbimlpBAQY41Ws1XCgZFvISEGDsy350DCeMm2367pYbRsIm+Q8pSYZhzVNwK2oslSSxM26O0I1ML275LL03eZPnBrnCrKlA/My0zDQGIEquoSCthKEWgruOyiaQugESVEsaDPB83fSX/OanHYSKYIUKbYX5/dABa36v6N1qFypxkjF++/ExQbXmWo4YILRVdEHjw3KpRwvBMdrZgzAQbGI1Q9R1UvQAtVXus1cg/n4FSHlbqnrHI9JlSwljBidZjj5nXhAg8vyY15id7w0lm6FGWEZoLGBdmnF+klTAllmHaQoiTGG2hMmbD8qnedeuTOmzN6bkFVXhBfQzzuUXOs4mJkpZHRkQILvDp996Az7z3hq5ETMVLPwuKFA0aenjXdmTdSRj5+1Qtxsr6I7JWCr2M4iPGXU8kSobZKGHctkUMLs1/rmnEXKSmehzFY3CnHZlK4KE5JIzgCP1+OcciHAtPkv2Y7JOZ7bXzz+/8yhyfVXGYvqaisu9zfkQ1YbIkzHwEZTXZQh2B7/ZbNb7mUVEyOpMEcMrUpUeCqcm0soaNjWHmrrvn9Td6ePaAq/uSEJlJru3ImnlJAoyDOWr+60SovDTJ1G77KtkrJxN6+uabsem88zH54x+X9kUrs0kHCVOg/us9t3rooQcFJn5+40E3EibqUMIU941FagzmEShLlDD3HPopvjLzFTTjZB0TVWQCcjvYBdeXY+1F238RPlPx5cyYaJJ+ZmHvqMG/eXXye3NIGgJ6NWGeScgvfvA0wx133IHBwUFccMEF5r0LL7wQg4ODuP3223HyyScXfvfGG2/E4sWLMTQ0hEsvvRQf+9jHsHjx4sL27XYbbWuBNjmpstOjCFEv4yOBzsZXENCKDsBzXHOsCCcpWzDmBHBZKwn+EdFxXNNKmBriUA54lDPA8fLPg1MF+lxgOkY9lH2LaFh4ziqLKmgdbIG5FTPR7Jtp5baf9haiv5kELE3Bd6T7HjjA/7CX44WQipTmffchDEOAC3NsuGCIogi+E2DGHZffa49DBNxsixvf7uQ9TcJEKmDN4hhRFKEVRrInaq7MbXJl3MP6M9+j9m0PLtzwEGDVhMnuq6CuITRcriyv1L5SSjraxzSGCxjCoDl2yGzf9+R5ooIndmSKheGMmW3p8EaahNGKF+XHr9pqJQzlMQShiKIIotns4PwHGwGmwmZSrN6h6b4PHYdFp09hx40B+mZkZvRMpAL5goNUOq+xZf/6rxh7/DC2fPox+O0JcFJ8HDVCMLgsXUeFOJ3HEQDIiSfDVyQMYwMQRAUr/c6+cAh40Qwivx9ONIQLH/gwNtNPAACq1Uru9uu/8kaEDzyE6Sqw3/WwFpKIK+r7rkX92Lb4gLGxAwDq52/7/Fevxo8/8zha7gyurw7hrZD2PT4NOtqftuZ4jG4DDjgBGoQgIsTYkVFS3J/Xn/B6fHvzt83fHkmOyweuOBFrRqr412tuN5+HYQjHk5+PKrJpWMjtv/h5v4Vv3vXnpm3Vyd+v2YLpgNGCEdQvuwwz11+POMwfe0b/+38AADO334H+N75Jfr/dLvz9KOZweYzF//YPiE5cB+B4NCOGVjuEkyFSbQs+kjMRzvsNTQ4Twc13pmfy+xNHMQC3g4SZaRaNs7omDAc4QHgLcCoQYeeztD3TADAsv0UAQjzEmnh2AggArf374YyMgDebRvnFCEPgEFSoh2lfBiPaLYHYCeBmyFlNwrgVghcvPxXYBFRZnzl2ZvytJkkEnhDwRo4HZtajzSOjLAGK73sb8zFnsLNhR2pyv8cLzhEAtNXCZMFkDFePq01a2pcFzWmADMk+t+I59bvdagM5lnEarJEm3WY2by60bcvLdO/Wl8duTQikydEZY4+YBx3sOn15Px7dM4VGu3xeR7QSRs1hGOOl7XU2netRhDFDlHOtm7aZBWTYasHxZk9Ea5jn43N4fjo5mTxntz5+GOf+wurS9idtfQTNQZmYwHj5OY0Zx3JMQKjxqU2AVsF5NaQbSbIqAWllmm3PohhMkR7EieHV9PxSjknTN9yA9thYShHT3rSp4zfL+p61iozD4ns7ardNhr0TN8HcKuKS61cHaztImC59mg1iTdzSNAHW3LMHlTn4qtt9yfapZSUbTBU+w44MM7rWmIXd73sf1t14Q07rHp5LyLse24qQd9QcWCVXo5Fzv7IoROQpK2tnEsc5HE13CtW4PyFncubSO3/rXfI7v/8HqD58eWH/YrXOguOkthEXzFNZXlJNGPYCgT0cEXrzmWc22hmLsPk+j6RWM4r6qNEAKaupqubjVY+iGXGs3zmGS07IT8TnjAFwQHls7Lk9IXD9qIytfPGxL+K3zvotAEjmbTQ0CVcAcPz4qwD8F0RmTqnn+iIzJs722DRL5m021h0+G2PVfWgEjd798xRiLsf+GUHC7Nu3L5c4Wbx4Mfbt21f4vSuvvBJveMMbsGbNGmzduhV/+qd/issuuwz33XdfbpY2APzt3/4tPvrRj3a8/5Of/AS1onoLz1GcZL22i8vfdvNt8BQ7PMWnwGgEDgYKxyJhZHsuWIdF3GqSXjhGLZmJTEWMu+65Fxs3HkAe4qgOFxSu0MV024X2c1FcBeAidipwmB4geW57HlfV5zoLWhEhEKn2Gw8StIz1lmxz4yc/iWVRskq9/vqfgvpAezqGN6iK/To+Do8eNtuampDWQUQk/RGKmGgoZv/RRx7BZKWCwy3AATGZ2b6oAJAZ4QN3VoGXyPcb1YV45b3ckCpRznF/Xv+ZhoTxmc4OlX0/fPhQR/tGe0zqfoi8lx68+1YAsoj6tq1bcM01OzETAW2ksyv37d9ntsVibaeWBBo51UoYH81G07Tdv38/CBaDihi33XYHyMatqWtQY6BZwV133wrgTADAeKPzOrjCl79bVXVYGJeBDoeFuP+OBzA91WnfNDNFAPQhdqvm+mU5x1GjrzUEN06TMJMzk7ntR9vAWqosb0Q/BJGTjMMTYx3thx/fgFpDYMLvR721FIITo4R5cvMWzEx1Wl/UHt2AlQC8GJgBBSDQjuPCvu9pOykCBgAOjE7ktmcteVwG24sw0JLKi8hpY8+O3R3tDx2aAcUQHOHggrWr8LMdu+Gomk/33X8fHt2en6mynKUr3u3duRfXHE623Z4BbGnaT6/7CRy/CsojPKnsTAa4g2uuuQZCUHz84CS+pYJdjZnGUdlUjmzYgIUAduzajcrOnagAOPBfX8CdKzrrcNjX670PP4yVACYPHCj8/UOjDt646XoMbPgJ2jcAL3zB23H78jPx39/+EZZmHkcy3q5qD+kMbNEGUfdn9je4ABq+PAZEcFAegwG4+577cWhnZ+bNgYPTGMYyc69qq5316x9DPPFwR3siKECAmEe45pprQHkbDEBzYqqjL/X1jwKQx+v+u+5Bk06i4cl7kFMPzKng/g9/BAdf/SrQZguBImE4jbHpyW1w248jcttgtAWHVxD6A3DVvQ3I4VHX4Lr97ltwz/iDuBQXwY8HJBHMk7E22L0bmiKoMODgoRmgCjSiFh5+/BEsVRu87rrrzPb1eRXK9k5jPuxPpduf3N89WzYCcLBl115cc83u3PYHtkUARrDmAMPgxJPYfMLZaI1FpX057+AWHFosi00ePtQ55tiQu5fUd/vJtT+FExRn2K/avx+2keGdX/sapgsKVZMowomZ97odw/ENAaCeMT/76fVwq8V9aTQdAATt6QkAFI9u2IRrGhsK25NY1YRRqtrJ8fwxUKPV6gNAEPMIAMX99z2ADXvzs+xXHz4MW4Nx3bXXgh/FHNO+Hp8NMPXiZhHLO7jbxzDkODe6fbrrNdM/M4mmyiNqTDdL2083HNQFg9b5ugDue+BB0F0PdG53fApYmtiRaW9xTr3O5/iGx1FRtR04ibDliU0AFqMaJ6TLfe9/Pw6+9rXm78rOncjSS2V9f/JJB8chuabGRscL21e27zCqQ4e1wdwqdu3YimuuOZTb/sQwAgHAcpI7j3bcW/DYY1gAVdrCup2f+KMPYOdvv/eItpm9Px7eRaATBdY/thHXTM1fEeHGno14Xua9aLz42Pfw3IN9PY62AcAFBMfnv/953HV4HMAZeHL7LlxzzY7U94YffRSMykBi5IS49pFr8Tz6BgCJargZdq557Lln2XXYt349lgMYnZjA+vvuM9+776GHMJMTXHImJ3F85r1///6/Y7VbToT30EMZnm3zmecKDrB04GC+n3kntNvGwumW669Hu8QNaUbN95tKubxp0yZc08xvH7V9dJIwyecPbXoI1+xS+0LUvA0tbNu+BVBzz2WN8yDwBTRnZlL7PfDgA1gK4NDhw6Br1qC6fTuA8mNjj9frH30M14w/WtgWAGYOAq/Y9BsAgH+76P/rzTWeQjTmYLv9lJIwf/EXf5FLeNi45557AOTLq4QQpdkWb3rTm8zrM844A+eddx7WrFmDH/7wh3j961+f+50PfehDeN/73mf+npycxKpVq3DFFVdgIFNs6bmOzX+cFMyM3STM8upXX2VslybaE/j7b/09IqeNgNUQOwECWLUIXIqrrroqtd3Hbv1a6u8NYjWWQwYWL3nJZVi5NC/0DvzHj25J/R057Y5ta/xk92PYNnYYzK2YgOX+IYa35LTfdu9dQCM0gfe+aZXhTUlq+/7jB/DwVjnAa8LmnAULsB/JKvUVV74CXuDgy/vuxH6mMwk9DA8P4UVqW1ff+V1MzsiC1nr7Nz7xJbkBxcCfftrpGL7qKmw/3MDfP3C9KUr+glXnA+iUm3PqY8EEENWVvZjndBybaGU/nHtvAwB4TBMnsu/Lly/FSzLt+x5/HBu2wTyQTlm3Ck88DkBwnHHaabjqRSdiph3jz+79iTxe6oG2ePFiXKm2dfv6m9RnVrb3pCScPO6jVquZfn5991cxvhMgnOGll70MtZWrzTVIWWiKVfa1K1iwbDF2q76PLFrQsa/8RtmZSmsUHBGoUus4PMR5l1yCWk6x9eZ0hC/eeie444Mpm7T+VlR4jZ134DTc+b1vpN5buLizL4DMoN34l3IsFKJq7qflq1fjqqtenmq76+qvYX8o6x65cT8Yh1EGXHzBBTj5tM7FT2NoGHv+54s4cS/whOIG/Fq1sO9iqwDwodR7q447Ibd9GIb4wg13AQDWjknia8afwGknnoarLkr3/c5bHsf6Rw+Bcm1HBmNHdtFFF2LZCWmFnY2bf3Iz1h9aDwA48bgTcdXzk77snWjhPx5OvFMve9llqPcNg979b3jepNzm4xXP9J880sCm//5zAC5qfTVcXHAcuoFxgf/41m14OYCFy5YgvltajrhTU7nHavu//TuirVtRveACXHj5Zdj1uc+hTkjhefj3bXfggn2JPdZ5k9tx+/Izcc4LXohzVg+l2kZthv+6VqqB9P0Utf4LfvXdGF5Ww1VXvTjdnnF88lsH0Ye0Hdkpp56Jl13USSDdv/s6YIdtRybHr3VrT8BVV2aX4cB//PALAAH8qocrr7oKX/n6lxEFwFCMjv3dvnsXNo7L16942ctwqHUIn7vuvyDAQBR5v2bFcpx/1VVgExO48XO3ApB1T9780uehMhjg67d8DZE7BSesIPT6UbNIGOYEcJQF3pWvugJ/++2/xItIDI9X0A6GUIknTZ9ajz2GXZ/4pHxdF1h3/GnAnifBHIKzzjoLB771bQACL3/5y+Ep5YIeh5b+/d9h/wf+2Pxu0XmdCw5Pt4F75Dj5kgvPxdVbHoTfN4SrruocowDg5msfwYbHx0BEDFfVpKo6faV9+daXPmNe1yp9uOqqlxS2ZTHH5358m/n7pS+5DH3DxeqTHf/5Wdiaj/OveAWqzzs7ty1vNLDlI3+aeu/KK68snec94O7EPVu3AQAuufhSDGXZSQt/8dANQBRh1bLF2Dx5CKvWrsNVryxWUn/mWnncNQlTr5cfx/+6/nZEYKj31zDVbuHss5+HE8/PV15nj8vLXvpSuAvKa3rlIYoiXHfddanr8ZkOzgXe+vl7sHeihe+850IM1/zS9vv2Pox417j5u9t994PPPWReB35Qer1/dP0NCC1i1RHAaWeciavOXdnR9oFv3AwgsSnVc0tO3I4+je7ahS13bAYAuD7wmit/AV+98x4I2i/tYwEsCyOcb32v9cgj2PWvnwIAHLdPdN3XJ6cfA6yi8331/sJxo3n//fjeHfL56sVNhMEQFo0sxFVX5WfNb/6gnB/kKWGOdtw7tOkJjAOYcQFqBULqExNz3nbR/bHhuieAndIedOWa8nFgrnji9s73qBDz8jw4ErQ3bMDON7wRg299CxZ98IPdv9DDMUPe9bhzrAHcfys818G/Tv0r4AM0+D3Uh87AVVedl/r+2L59OLBlHABwshtgwamXYvTuhOwFgKq1btKw1+tl1+GUENgPYMHixTjrVa/Czi9/Be1HHsG555yDvss7x4Jo715s/9jfpN4787wzcfHyi2d3QHrowcKzcT7zXMK9++8Ffpb8Pd/PPD3vAICLX/ACVM7OX0sAwJ8/KOf7LzxuBLdvGcXa407AVS/PpnlJfO4W2Wkqkpowp4/txE3q81WrV+GqF8h9+cHnpTW94zEsHFiOKSRzrFZlBH1B4T62rwABAABJREFUOq452W7jwNe/gUVLl2LwjW/A3t/5XQSnnVZ6bOzxeu26E3DVy/L7rfHf/50mXZ6quUYPiYPWbPCUkjC/8zu/g1/5lV8pbbN27VqsX78e+3MKGx48eBBLliyZ9e8tW7YMa9aswRNPPFHYJgiCXJWM53m9B0IJtDyaYQZBJTl+NZWFx5WNS1JLQ9mRUdpxXJ2MEqaiAs6ER6jU+grPg191EDZtRYVT2DaoyfdjpwKXSjl16PL89opQ0oFuTcYQglT7/mqA2HD0Mmh06O/+HtwiqIKKD8elqPkBYiVR5NQHRLItMqYynEVs3qNKHSIU4eE48rgRx4ELZpIFPb+CZo2i2khnsnPqoRICYZ8ihCjp3NcVp8Fl18vtxPocyv1xc67/tUMrsAGQJIwAxPQogHUggqHiBfA8D3XqIDY1dRLfY70toYt3W3Zkyw43wfqlGqfmDpi2Q1ObMY6VoCJGUKmm+uPGDYSKhKm1AzTi2Jwn13M7+i4GVIF0CIR0DBUuA2WUtdH//OeD5lwH7pCLGAIuCFoVGSwLwqjwGhsaWNChhAmC4nHEjZsggkEQB+1AWp94tWpHe0IIXOVP6rEaGoJCqBoZA339udv3+pPs2gXq+UCc4vvjNSe+BhszJIxfcu9FfS140xUsml4FAJjxx7EiWNvRfmRgCMAheMruboZSY0fmlxwbAHjbaW/DH938RwCAildJtV3QT8AtJYznyHO+58CD5r0WD5PvrDwPjpVie6Rj+4HxJg6PS+XZ93b/EPa0J2+b1VNPRbR1KwYuvxz+kDzHfHq68PezVrD1QB6rmUh0fIdZEV1NqHjRJFAFohbrbA9mCvgRwUDVd2KW33ehPs/akRW1J0IeYeKmx/jK1Kk5fVdjvmAYqA2gBaUKIyFcUQVzfDiuvD4IIYkdGY0x0lcFAuXR602iEi4COfNc4LYtyfYVaSoIR7Ue4PnDJ6LhTaE/HEboD6ASTZg+xVb9q/GlFFV1Dx8WERxVx0eP12bM9n2IMET/eefBnqnMx5yBq+eh71AM98n9mAk7z2fyBfkP5cyMPzws70ts1d4anRorbSsy9Y4o6XyGp9q30/YELinuC8upPeY5DkiJl3Jryu5PeV90oc7+ihx/woJrF5BJPlTbken6crz8OOqaMJ7ykyFl/cn4Rrs5c6G54Nk0Rz0w1cK928cBAI/um8FLT66XtmdWXSJBu9933K7RwjvH0tS2GRCJ5P5wAPCC8xppBSIYXjdwllELcto5B6GEGPsv6nEMjCjykLhgTgUuayHevj31vfFbE/LzxY8KfPI1Xa7HzPODseJ9DZH0R5O3PMofZwRjSV3CHBLmaK9Dqu6N2+oVCJt/jYrnW92QvT+a1jXTisuvgbmCtxrIjljOgpGn7P7c/IY3AgAmvvwVLP/TP+3SuoefB+zrkarxyK69Sf2DaESda1KKRPleocDZi8/GdfRJAEkyJOnyTC77TPeAeTX88JMPY6TvbCzCI3BE/j2StdjhBKj4lWfNs6iHpwbPpvnMcwl/eddfpv6ez3NozzsAgPKCmJ2CrqUyUFXz/YL5O+cCugoC5ZFJMFzaTGpYCpKMf9r2seIwnP3S1dj6oFWT0PFBEKd+R4+p1HXh+jqhaPZzjoWP3AvvytNK2zR5E9RyKOjdO08d5nLsy6u1HmMsXLgQp5xySul/lUoFF110ESYmJnD33Ulhw7vuugsTExN44QtfOOvfO3z4MHbu3Illy5Ydi915ziE48QTzWmfgaCspDU+/r0kYkiVhOrd7/IInU39XIxUgFwxuQXFwAHjN/3dO6u/F/FcL2/oVOemN3YrpS81ZlNt2qF9JD5UKJalnk25X9R0w9aY9MbWt2qgj36+4TqrwtLACwlzXUgC3vpcmYcwCmAu4YMaywXF9TA11cqucehiw69o4nZnF7uLFcIT8nb52oPqgSJicIu4VT22DUDjCRTQjH1hEcHgqWOnSnAzmsNND2CZhuLJTC+Ia4ijJal4y9qBsyxmcTBF3XU8FAGphBTsmpkxNGNfrDOARAtSX6EBvwlo7LATcfG6aEIKG+qhZlSQMActtCwCVoA6Hpfd1oK84Y5wA8CK571rV41Y7M4CXfOCPDAkTsComCTW1PaoF90fl9NPN61N36oulOLBJCcV3L8ycuwXDhe29umy7dHodAKmEqbqd+zo0ICcJQSzP6z8ND4EqhYLjlj+Oal5yLfhO+rjUfAfUGkx0gW/uFQTvgj5D2YiCgqOzQRRz+CooHboCX7xMjSU5RZUBq4ipQ+EoYoxPTxcWJI84x5JGUnh19eFdAIA7thzuaKvrUUBwcz/VWjKY1m50WiIxLkCtmkyEy761w3z7JKj91ASPLjodhkX3gLq+PHlMpvulQqtRX9rRMjL1MTg86pnzy6gmqj1DhouYGdKRE4aBqgtfjc0tZWHmnH9JavuxmjQzR3qVv/O830fTk/d92x9IF6BV5+LAIOATisrEHvPRaEOra6ygrxCmQCQpsDk9GuiFhO9SU1i+VXjMgWnpzQciGBxVk4pExfk2Io5lbTT9eyXbBgBuBS+BpKBl4fZbaSJalBXXzivy26Xwb2vG8l6Oy+/lWNXs0GRmWaFOzoQhKXVNGMHL95WxpCaM3EZxf7L1b6KwVdDyuYdWmBy3yWZ3f2VunXfud1/WcJLcD93qyBIuTB1AAHAFKS6UKvT8j2PQ60+UMDTn/uMCXI1L1OVwPQeRsiQNffmcjA8eTH3l0Kc+Vd7ZDOIwk4zDiq9fEUdmjutGioQpGAvsYtx5JMzRYvyb3wQATMYu7jwlmYfwefQ5t6+rZpcxb66I1HmLrCnWYC8ztYcCMDX/SC+XKNpRznXJGYQaTwjlcKiDhQ2pXNY1QLt5OD5oJSd1QD2fn3DPwO5N43i4ImMsouBZKaJMUoYAXP6UhpZ66KGHpwi7pncds21n1w6iHRa0lIjUfGegKsfLovl+yLiZ4VEeg6qMxsWWAltYa0RG5bzNpzGWnzSE/sXJui92a6l4HgCzpiQOxVioiJ2StUEWx//0290bWZsjonz87+Hpg2fEk/LUU0/FK1/5Srzzne/EnXfeiTvvvBPvfOc78apXvQonn5xIyE855RR85zvfAQBMT0/jD//wD3HHHXdg27ZtuPHGG/HqV78aCxcuxOte97qnaleeVVj3vcT+Rwf3OdIDi2syluXgpxe/Rk2SE6SvNjfjXUvehLV75EKsyiVpRnkM1y22pegfSQeg636xSsqvqJoeTjXJKhLV3LZeoNumSZhsRfj+iovYDOXWwpEmAUNtqxJ4FF/eu0NtzwHj1sa43kbywNBKGG39pREzgYvpIykSRuQQJkx5BRsSJue4S1WSfL8a6gKPioTJCZAHvqU8YBXEhoRh8NT2CSHwVX+I6qTgyX5pGzXbjuzc9oTaZoC29dCNlWUYFQxEESV9L3mJ7Es7CVRX4gpi0TTXmOcVkCqu7A8XSf0Xh4flWdfq+GglzL7FxRm61PPhRWlvyKBSzpB7YboWjZ/T3lm40GSqBlEVU3BMUNp18/tOgwB3nimvnQHNC3UZ/aPMprzh/IJ2AOAH6etp2h9H3e+8nyp9cn80CXNbrWqUMDSHGLRRtwiVwEkHuwkhqFjXqJ4C0cFOyxjZ4bpRwhwNCRNzjkDZCoYuga9u04ay0cxCqwiI44LW1f4I0RGoNttnAoPhjPl77S5pd+jlHCsdYKOCmdFnZEKSk1GbdQSoYy7MWp0Ibu7BMCw4HoaESduR5QXtORem/hT1O69Je0ILALFeyAsOl7qoKGUGU+Mec3wQRTSCs0SVSBnWLqwb0qblyvtHrDgu3XVFMjBHTq5rfh8aviRhwgwJYwg8AgTEQf/lf2E+23joEXUMrH2xCqyTIMDwr72tY3/zsO+vP4Yd73pXIQGnEcYcEAJXbL0Dlc2ybkGrhGy4d4+UylPO4CoSxolLlCphaEgqAPB4OZHEMouHbsQHV9c2qaraalExCZN3LEQXEsb+fd6lL0yTML68flpRcXtmfaaVMKIkiM25MAPPOJPPozLSxr5uAGCmPV3Q8rmHphV8nA0Jw5h9/5YTZQBMEFN+uUv7zDXlIFFUZaF/moNjWXVBFxKGmzklVXOR0JPXwFSfVJQWJYTMFtl7tYwUBGOmpoSn5hdZEsfALjp7DFaRfFoeh8sfEgg961k3jySMfY1NtUuI4SNA5a+lvaNnDV1l414Pz21wNXA49rpMUIzOdAYZBeNmPCGOgEOK1ytF2Duzt/AzHeicICO573dAzQvt9YKIe0Whe+ihh/lF9hkqomISRgiBUM13+lUcpWi+HzIONQVTShi11rWe33ZsU8/biCtjeq/8/VPMZ5HbaYes1zAROD5wm7IZ47NP/Nh+6nld28TW9p7PXjTrbffw1OIZQcIAwJe//GWceeaZuOKKK3DFFVfgrLPOwhe/+MVUm40bN2JiQgZwHcfBww8/jNe+9rU46aST8Pa3vx0nnXQS7rjjDvT39+f9RA9zBJlMMoQTciU9sGjSociOLEtkaLgkxOChtAUd4bEhdfIQVNOfuTnBP/OZIlb2Lr0gqU9D8hfWejuGhFG3Dc2w3QMVD0zZa+UpYThJBvGK62AdSwL03Mq0FI4qlm1tw4lH5WdajSM0gSCwiEyAMtmWVirZdCrZjuoCsOq459i+AICrnkQ+04E4pSbJIWEohckYqER1xFuUVYZgqcWEn/2unXqaY0c2MCgX4C73EVrWN0zVESE8NkQJVfeyVpAAgB8H2DI5as6TF+RcM2f8Eqijg/CJ5JRwVqoQCRXx1FQkTLPEq54QgopFDgFAtVZ8/d72vOWp/QAAz8+xeXJd+KE8Rgum+zAjEiVMmZrEyWZHlBCaALB1abp9lviwMXRK2nJoJhhH3eucjFRqFgmj+kNV1txcSBitfLBhWzlwdT2xOFEivfGkNyaN/T44opMUnCvCWMDTZIQLrD5oBQPzAkZqUkhcJ6Wa4BnLJo2YcbCcQTJvMqmD0VqhAgBe3DJForNqGEmUqHNg2ZGFedmXdt8zdmRRTqCOCZHc217nNRm107/BzAKfwyWuIVUipYRh1E+UMCwhYTgY+gMXFUWyTAfjAIDJQ2lSS9ttcVduzyEOGko1E/qZWm8ZEqa2MEn0MFe0TdpY5476Ppy+ZH5RRiCMfelLmLnpZjQffKiwDQC0Y47nH9yEd979NfD3yMKLZZnb/a78fSJiOLHsm1tCwvB229i1AYAj3NJgbee5Kyc+NMFI++T9WxqgyTleokvGmP373VQ5sTq3taA8My673UiRd6Jk8/Yx2zy9SW6jjLRpplWSO3c/VtDyuQc7QH7d4wdKWkrYxMtsOHWt3JZfKL9mnMwl4jozZnHfsV21KQGG1bUlZowUtPP+E4LLcQ2Ao+Zd9fZCAMCjp/+mbJTNaJ8jKZO9N7MqtlR/YmbsyBxtR1bQ3h7XYjVduv842Vf/hM76YBqH/v0/8Pgpp+Lx9Td0EPF5+N8Ljl1Gp61mmpgF0Xe0GPvKV475b/TwzIRRwjjJdShAcWCqbQgaA85M4iChQDPudBbopoRxePHnieKFZN7Pv0f0WNC2hrgvrf9C6e/30EMPPcwVWdIlm8hkw56jDRgSJn++z7lARY1jlCc1YexahNx2a1FKGLjyO7W+ADuG5Pw99urICmH02nmGNaCH3tkkC5l9cbrbWz12KFk/nLP9FbPedg9PLZ4xJMzIyAi+9KUvYXJyEpOTk/jSl76EoaGhVBshBN7xjncAAKrVKq699locOHAAYRhi+/bt+MIXvoBVq1b9/Dv/bMQdnwL+KfEo1OQKKVgBaxKGZ+zIBC1eMfthurgRFXFp1o/jUXDXUmdUitvu3TwOAGBu1arxkj8oDg/IAbfTjiw9SR2seoihSScrIGxqyVgkjEdBEZsAKRNJX4lSfNhWbZSooCLx5bbVpD3mAlvEMhMocIKKCVba0EEHY41WcOe7juxPkg2tiIwcSy8QYmyznrfnMjBlK0XA4Fl96Mjat4gVYdkhmT6offVYgAPxwyZjYF9F2t9RwQBN5qkAqMOSBYLHaxAIjR1ZHpGB13wS5ERpWcT5TvP2xNAJpUWghQoox4oQ4E75g7Q+syf9d634moyH1qZs1QAgyCOQODf3xsLW6WiBmMzeMhLm/Ecygf6g3Gf/3hMzJEyOvZiG7wANL7lfZ/wJ1P1Oa7RAkVAU1BB9umA6zblubdRdSwmT0xfHJs/UNRNbKpL3nfe+5HPqoK6u7ZgdeQAm5okdWeQA9to2P6s/Nr9PHAfQtZEKlDARF6naNRpjORmSPJT7QQXDw2tUQCwWaLtykW7bNsm+C6smTGJHFhapA3R2kCJrHC770Gp1Hj+pOFDEYCD3ccHBT5rPm1OZvlgkjEMdQ/hFjrZs9BP5dhxb9pdMqe3kOHS4Ju+3Q7vT95FWwnBXbs8hDqYDSWzvWyKt43RQMNy2HQBQiQCfuCCEYLUaX0nOuTAkjCx2lbIQmvzBDzra278FFJB1FmIusHIqCUQHS/4XzSgqDGIurUjlKBEMrrJDdOJiwjVuh8bjWCOPWDOftdILmbhETSKEMPtHq5LCKrMjaz6UQ0ix8gxyW7FSRghxLkyQvE8lYZQpYfR+MRInSSQlCyfb6kmTh2X2Tzxzz49//J8L2z7XYJOM/ZXuxIOwyYJZBPdtZYoX5hPgclMCXoYY9P0DhXZk+m0KjsrMQXCixi+S9xwXiRKmaI2dfYZ0Ca529CdDSnYEdC0IloyrDtMkTEFb6x7WzkO3nCH75i7Kt/WN9u3DwX/6J/nHG9+Lbz7xzcK+VFWx3Y0rjyUJkxyL+bYjs3HvCT1rkB7KoRWafPAnyZsqiSWr0hKMJ3ZkjkCb5YxfOZfc5MnLzeuyZYsmW0JhE0IwNmUd7SNtx5u899C2O4p/oIceenhO4Lwl3RUcc0KHHVnx3M1+vus5ZLtgfRBzgf5I1c9EDKrWttQKlN1/4H7zWq+XiCe351AHLVfOmSK3hvFWOvnWJHpSB1wnKJck6PFw9mSThsOTATjsnypp2cPTCc8YEqaHpxkm08FlvcjktIBpztSEaamgfl+OdE+j0hpN/e2wsDQbH0hqvQBAUKKEOe1FyYRU9ykcHMptO9Cni3H5qfYzA+kgdsWjhhxAjhLGJmECz1FNZJCsLSyPch3JtUghlyQPG059w7QzLrCSHEyTMDkqF9ZBwuQvDD0qN+wqEiYpbt+5TZusOOXghWBMF63mcC3ixXcpHuerrf2zHqRqoUEtNYIn5L4OtRbjVY+9F80HpcXgjMpDJ5yBKNutwde+BgAwMJkU4RbOIkSibfoeBDkRDr8OulRmuNM4CXAOjT3e2dbe58xxYDnHxUa9kVZz1Sol6pOgH16WhPE7gzfOggWotg6Zv5s8MPtK3dkv+Mts12QDggfXye39+VsdYxFVhN0DT5jX0/4Y+v3Oe9v1HdNHbUk2WzuyqpfYm40EndZoDk32R3uyNtoJMVTP1IfpU2NWxI+chIkYh6cVIW6ahOkIoAFAzBB6ffjuLf247ZtPgCo1TNFkMi4I9uXVhOGR3AbhMYYHZCDeZUBbTQ7bGRKGC2HUfEQwU0Q6KlLCaF9brYRR5NVEs7MvsUXCUHUNP3HCVmO315hM7y8zE1IGSqi51rQCgVHfKIdSShhVtF4/Fw7V9gEAxvc3DMELdCphmGDYMyBrjzVrS2RGugre7v3whwEAI9OAr37nOHVi22pybgd6eTupB0MIQf8rkyykfR9NF6m0dhgCBFvXvBIbPv/D/Da6KecACCb7V2P96e/E8dgJt/+RwgWFsaXjDI4K0DjCTZEVNprTDUOma8QlQckoExQqa2svNGil0vFeFnkETTe7tpQSpkQ1w6xzVp+FEkYrWxhNSBiUdMVWDazd305tIwvBmLFWEiAQIFi8dbx4488xtOZoR5Yiu2alhLFImBJLCy6A5ZhIvecLUmhHZohRwuCvPA/r1yR2ZB3XthAmscfx5Pf2L7g10wHe8R2NyXz33BSy90OZnR4YM/2hsyRhOElU31oRU+TTHm7blvr7n+77p8Ku6O0fC6szjdg6tkUZsvOBm85UFr/nnnvMfqMM2YBOD08/6EuRVzaa9zw1P+8Y/ywlDKV81vPXA5bVcXBwsrihfj5b689GbUmhnZ4mbag1tLzlxiO3+O2hhx6eHRgKhuZ1e9k5FJssJhtCa12gSZii5zzjAjpKxGhsrLYD18XyiRPx7jv+BZfe8JtmraMVzFQpYTzqoe3KhM/Iq4FlCw2qedi+1oFECVOyDpr+6U9Tf8+mFp7LktjotDdR0rKHpxN6JEwPRwbtiahmXloJ0/I7F3nnLn4+GI1T7YwFWF72+zt+CCw72/hSG/CJVKA1D7YQoMMGy8LCldLyiwmWWKMVWD34viaYNAmjsscz9TcIIQiCdEF7IFH/2AvKikvxV9GvgqoF/qTog/UFtb3kWHokAlc1YpgTmMU44wJ/630OKsEbTqWWr4TJ1ITJq8UDAK7y33YzSpg8OzIACBTjv2HJjWBWLRvX2r7nULw+/At0ajQBkWNHJpwF5vXKiZPx8P0zZl8BqYgiimjqu+wyAMDS/fegf3Kb/D7th0caCQmTp4QBQPvkMa80t2PSl8HbEzZ/Kbet2Zcgfc5FF+Igi6pXLCvdeN758DM1YSo5pA0NAtQscufqekJwOV3UJDZYvZzQ/PTln8bfvZHiXb/j4PHVidqgCMsmn2deTwWjqPn57XWwcuXEyYCwlTDlx3IoGIKnyMSLV1zc8bmtvtKr2v+efqKjnUZNbYsdhR1ZxIQhYWIHqQzEPBslwRjWn/EuzDQpHvzpTghHqeQKAs0xE5jyOqNtC/s6zx1TwS8qGCp98h5yGdBSk8NWxo4s5gJUBZebXlITJioI1utsHn2vaiVMXgxA2jCpQIG6hllAUWlJwmZyPEPCmAmzKmxOXRAAlVhaaz16+m/iYCiD+IKxhPRXxLYmYQ4HcjxiEUfsJsetNSQJP62EmYlmsL9/q/m8UV2Um0GvSZgBtS/TOdYfQmXSkyDAoek27g6SWmS80ehoD0j1y8GFZ2PrulfjTnJpbhuNiAkIAmw46S04tOh5eNHWXwJxGoXZ2zrQSkQMN07UFmFB3YPmZNJGW9llLcdsZLPri8gdIL1oIlV1/kpqIziDgwCAwxXLIq5EOZP9/TIljB047wu614TRgX0OliRQlJbUUB8KjpUH49Q2chrLzwnF3ed9CPec+wGQYxcHfsbBtiObTYDcHj9pCc8AyHmErUwRJerqiHFcRNLPEE+QQiVM0g0Ov28Rxvo0CeN1KJ8guMmodNS86/Bx6wHA1HwbeuMbUYSBzqGoAx33Q5mSK4zN/Jxy2ddsLMFAkzDWI1fbEfFWQcdIem5CiryIkQQo9Jy5qaYSjj9/wd3IIk2LCO0s/uuR/8Kf3fZnKWuSIoReHx469RUAkckfrcce61rf6liAz1gWt90Sb3qYdxyYbOFb9+3KVS9rsJy5h54/jzfSE6yUEoYCy/uW4wenftp8zombe2+dsDVZWxz81KcK+6Kfz8Jaw071rSxUsE795DoAwJB1mZ27tRda6qGH5zpiMb910LJKeTZZTDboOZpDSZJ0VTDfZ1wYdSBBbJxVPOrglRv+DwCgHg3h4Rt3y/Y6GdxN1qs62TF2rViegl47b5rYbNmRzX4uwGcxb/C4FW+Je+rbZwp6T8oejgyqJoWe6/EctYfG+857vwmWZe3IcgOva18EvOtmAEBtZp95e8bvvupsTSYT1kpBkXIgqRPiEAdbVfFuWkRM+LKvWSVMXt+DIBkI9bRamCLSyUQ78Bx8ib0MvpDWNSGS75EcEkaQpDiwfABoOzKO24IK+mYUebJwAQjPsSrKWKnlETUA4ClvchpppUFJrRFCsHT/XXL7AGKmiB5wuJYax3comkhUFOn1hiZhkofM+F1pKSfdcBPQGDX1cSlnhjDTahwCgXXbfwQAiN0alol9CQmTU9weANyRYQBAf5Pj+nV/g8tu/G24cUmGGNJKK9mZuT3s3NXFdojHH7cMg5NPpt6rFBBIBECglGJVc67K7ciyaA+Wp9K+eOWLccaSszHWL/exmxJm61LLP5UyBCX3HwD0tYdTZGW3vrvUxc1vuhl3veUuUwPEhm1HppUwzZx7QYOKJGiaRWvDBuz90z9DtL+8HkHEOF6wf4N8nbEjSwU/NBjD5GBSNN7UIykgYSLO8ZPV56f7zVmuh73OeCU8Bq1J0qFUCcOFOf4czNiRFZEwMCSM9s5VqpKcAFbMubEDdKtywsoCB34kAwETWRJGTzLVuSCEoAKC4eZS0+bGLat1YzOm9lF5jDVByBwBAtmv2LpGDp4rg/vClYuC0xacBkEEDtYkEdOsLc4lYQK13aVqHJzUiQG2nZg67tT38bpP34b3XP1wx3ay4DMzaFYXmr8PjB8qbMu4ABHAdL8cOwbD40AESQWqU9vWynfOQCBAlRombOYviPYekOeECGYV5J5drRSgix2ZFbihlWrHex3t1cJp3O9DrC1L56KEKakJY2e+1/3yzDggsW4ShM9OCWPIr6S+UpH9kw7GNqpLMNO3AtP9qxE7s5A2PEdgE4xlRJmBRXblWQbaiKzC1hLFzx3GBSb4UOo9KmhhTRg0FDFNOTy3mtgpUq/DdlBwDqaSY7Tlt+PrsauG2KnAXbgw9R3vwhea15uXFXY7+Y0MCVhmpxdP2LXxtJIr/9hkSRIAaCsiSTTzrTWziT9OyWnV2fV6+1X1GGfh/C1ZoyNQwvzjff+I72z+Dn689cel7R5ZQ7D5+Nfj8JLX4JzRv5Nqt2YT+/76r4+qz0cEW43Y1xkg6uHY4g++/iDe/42H8JHvPlLYhpv5hMCLt7wRb3zwg6gROSg8uDO9HgKzlDAucPais7F7cJP5OHaDrraFUxMHCz8zKjSSzOcjr6/wuT1z++0d7wUlNWd66KGHZy9OGUmK1M8mWWEu2P2+96f+PvSpTxe0TJQwnkMQqNhCkfKd8YRyFoIZJQwTFD5P1pETh+T6yNiRudY8Va2zJwbXpWzM1A8AkEkrOnGlLCHDWZCe9/FZJIm4NgkT9cbfZwp6JEwPR4bKEACAKiZYB8WQU1fFoY6xjREDcnDYuELLqYsvwbVfuxousxd0+Ys7G75VB+bXLlo7q3a3r3y+7HpBJr7rayVLmshwcvpe8e0MdV0fRhNPybGpeA7a8OBAkllMJMF2wXTNmaT91wb6Eesi1ZYShnPgo/WF8BkQU2BozYkgojNAm7UjIwXHXSthKLStGi1sL9rtxO6GB+C2EsY6loGuJ5N3eEVnTZhsXaEmHwCu/RNEIsnwtvsz+PrXy75HypPTq+H7tT5zniqVfMWHMyLJi4FGEhDgXRYvnp/J5uxSEyYL3y1Wk5w0fJJRFmnUg/z2Q2/4ZQShDJrUokQ51E1NYqO1eLBrm7UDa83rspowANBcVsFXn/cx/McFf6Da519jL3j1OgBALeoH5cl9OJu+9/l9qHn5FoauRcLoRe156l5a4NY7v9BWmYFx57iy9Rdfh/FvfAN7P/Sh0v7Y2fVLxs3lDAA4+Il/6WifnXg9uvqX5fslShg/4wlTi9vYMdroqAnCNBkgOBxFwngMCB15X+TVhBlQFmYDTW6KSLOiALwmSLQSRmULiZwJIhMCQhO4VTVuOhF8Rd6PHUiTnYltU/LbfnZ6QpSSImamdkGsrjHbptKhmqxOJs9cqD4oEqbqVhGAYqIiFWXT9eX5JIwa83VfGDqPjbaSI0GAneOj4PUdqc+feMlLO76z5wN/bBSVAPDX//mZjjYaEeN4z8PfTb23rD1YQsIkZAAAo4YZn86X7s9Myc8pCw1hE7VLbL0yAegywsYOPGs7sqICv3Lj2uaIJHXVuihh4lkqYZgVgK4pRWNY0l4HrDnhSRKJKLGusmzg9LEvUsJossmQsAAmL5pn/+xnMOxru8wyTsMmGyhIadH3LAkjSPEcNOYC02Ig9Z4jaKFyQo+FocvhejXESgHOHA/N6XQSkQgjMFXgdedkE5++cTOIF6Hpyvu0WV3YOSatTQj8pk+wpn4aypBdvOfkSBnEU1bSgLKEFSz/mayz5W0lTEsrYZoFyVKZOeR7v1tikxXp49b5+/OlJrHVTLMhYa594l7z+o9v+ePStmdsF9i39ALz9+jIqQCA8a9ePdduHjVSitxZ1EvqYX5x22ap/v3hw3sL22iynnt7cPr+izHSXIZV03IOt2NmI7ZMWHbL3FbC6EQBgUjNe2ZD5jsll7uuW8iQjJGceoXP7XDLlo73aIHqtoceenh2w1bhddhyHS2y6+SSBC2dKOM7FBWvvAakJGF0Qi+Do+KOYab+5WhzFEIIQ8JQL/n8bCEdEKb7VoJn1DBa9SLtW/V7ZbL69Pg5KyUMs2JFx9LHtYd5Re9M9XBEYG4/mnwAA6frwaVYCVN1qyaT1D29hQUXjcFdo3xkS+yTvNWr4UXJwpCX2BdovOP/vgjLThjEa37/eVi9oLjejONRU5uiTytPClQNflXu25TKRC6zUqta9lE6yJYoYZJ2MkBNQFVNmNR4rBtaJMziOLaUML4JMsScG9ImdoChyjBIrTO4zmnWjiz/uPuKZHD0+VRDRF6tkfFvfjMhYViAWBUGEySthKnquilqd+wASf+MIoVSJEz6gdMQ/cDBDUmtnMznA6+4AgDgxfJaid06/CghnGrVfDWJMySVMAMNAapJmC7KFjdjR8a7eMj0X/nK5PfiprHTysPywX6zDxpVr4C0IdRYl1XYkHxPsMJrGIApAq/BBnOIiQxs9Us3JcxAxcVE9QC4OphFShi/Kq8Tj1XgiLmRMGWwiT+hrqcZVRPm8oXnHNE2W08U25kBSGVE+7FI5WA373+go302gDTaf7x8kTMhE0Ig5gL1KJ98fmxvhsiIEiWMW5PnVtqRKSVMxo6McQGHaRKUIQjH5XYaBQvobE0YpYTJJWG4SJQwgbxu/HgKnlbC3H5fur0Jolp1swjF3avS9VLCdkt6oqsxtaUCDj5N7hNH1c/SYx7xPMSxIgD8JPBXpx4O1SRhMjGwLt+OTClhXHUdcZajQFIkjPB81Nd+CpXjPpv6PN63D2w6fV/L7NHkeh3ZtxpFYDnZ666gXe3INKmmFxTNRv51xJqhateGw1Qmfpkd2RyUMIZAIQRE2xPOQgnDCTWE+JyUMGV2ZNZxTOzIymrC2CQMU9vIUbeZ9vr+6E7C6EWWTcJsHCguMvpcQytFwsymyEvmz5LaJ2HMzdgAdCFhGIer7tOQyvvHEU7udSOEAFErbE4ZXK9mbHg5dfGzh3el2o998YumFtPGQ1P4vz/eiOmmQMOXY2TkdaoWmK1qEKLMXUz+buY4EFFcZyxVv0iTMEX3hw7UWocuVIdUzJKEOfvh6fx26FTa/PR5yVg59pWvFn5vLrATKFqzuMb+7mc3zmn7lCXPmsjrPtc6ZrADOj0S5mkJ+YwXsCeQa4ZrIM40rt79h3jtd19rNU6UMI4l6AsdOT4xt9JVCfP8LSXXgbYatBIDOXULn9tZhR+Qm4vZQw89PAdg16jiXebuxxI6wcp3HVmvGUCrIKEn5iKxsRXcxFaa4y0cqu027cYmJ6X9sYkBJvvnVZO1wejwWekfMOsayy2jrDZm5rPZHEdbCTPVKF6n9PD0Qo+E6eGI8L83L8bnD/w3frjmKjh9sSEaPHRmty2oLACjclDxqYPFa5q4VNcvKCFh3OFhEMtTMq6s7dovz3fw+j88F6tO6SzcbYMQgvqwDMwNqVGxyI7MU0qYSAVMjBIml4Sx1QLK6kcTGpYdmWbmqVL3cJHMpvVC3o4N/C5GkiLVTgCugjxm8g65YHWpi+mzT+roV1ITRpEeBcddW69R4oAIagKpeecpPnTYkDBLxy0SBjwVENf7amDVs6GmJkzy0MlTwoRwIXhnWwBwl0lfDi/SnpwVVEJi/M0rQZESRpIwC6YsJUw3EqaS2Rda/nDsv+xynLzpq6A8wpmPfraUhDl9+YDZB41akN9+4vvfhxfJIEbAJOmWp4CyUTkpfV2wejmpAiBl+2WrDfIwWPUgLClIUU0mXVfHYwFoioQ5useRrYTRRN8e5W1frwx3fmGlyjwnbudnCuzgQdyz757Cz+NsprH9R17AoygAnTPJ0kHjC/Y9lvuV0YzHeGzVhAmULVMQJyRMVgnDuEhIWcFN7yv7CzKUubDaAo6abOcVe46j2Iw1jqoFEg4Ko4Shu9JZoUm2rkXCCGHq2WjQ/3m1rAmjnje6lgIhxBAxLpHnvPZqGbhY/ME/RiuWZE2lZtWqoq4hYWbqy3JNjPQ177Vlv+PRJ9UxsOzI1HHnrgcaSFuxv3nHyZkt5W096Qvlxdd+pI6va40NblwrLFiu7ciMEkaTMM388+pcJ611HB6Z8TwqUbfYBeiB2dWEIa4L4qnnQ0lNGN15RikqivBqb9xY3D7z+9m+pdqp69ehxDyTygL8iR0ZMySMI4rH75Qdmc58Kwh4a2LJJmH2TpZbHz6XEFljSpGPt42szVaRDRwgiXORsiMrfuYzLuCpOUrLk2MRFU5un0QUmfFUUA7PDcBoYkf26P2d17G2uNUW3oQ5aLlyrJGB+8y9Zl1PlANu+3Bh34HOsdlB+tjaSBM2uiZMgRJG3dfRHJQwIlsTpwTGEklt/8llST+mfvazwu+xyclSFZQNO4EijHnX7x2Y4OhvLUAQ1XDy8Kldt6/HUiCfUPt5IUViP4VBsR6KIWvCMLg8eb74DgFxk0QbHdwUVhKKnUwXOvK+i51OEiaPKCmCUIFKhjQJM5dtjJ7aXWXfQw89PPsQW84N821HNhfouX3gUpMQWjSX5MKqgCWYeXZru1iNHXv2ImxZ6xcv2V60fIN5na0LY5Qws7Qjy66RmuTRwrYarqWE8biPVo7DRw9PP/RImB6OCI8JGRC6qzKE/z3HNURDMydbfjAYxFBlBQCgqgL1XBQTGTam6yvM61XswaPut42hRTIwN8yL1R4AsGBFMqByQktJGNvOqlMJY5MwmuxQ2eTEGuw12WARAsGaFxq5OXMCw4wzLgyBoEmYzS/urDvCskqYguMeDCbqIY/5hoTJa+8uXWqyrfv46ZYShsErI2EUIsbBc2rCZEmYUNSwb5qDmEBl+uGlyQVdzBaEYt1MYK7JoJpPHrjKjqyvBSwZV1nPXdQYfi39UHa61D0ZuOpKrNhzKy655f0YGdsAlxYH/AkhuOn855u/eftnqBUoYUQYGhLG45qEKVflrPiXtD2W38VeDEiTMHl1WLKwLfS6kTDnLjjfkDCCiEISdLZI2QMe2AgIgf9VqptNjT2dfXX0sS0Pvnzwlg8WfhZZmTWCpDMAw23bOtpnY7J+LDNu8rz6daZuPTOZOnO5LFafnVBGVk0Y9/aEOKI8vyYM4wKCJvffnuUvAgAEM/nXEbHsyPwVi829j5ygHovaVk0YRYAsOskoYSKvLxX0MgFvm4QJGyaIqeHsuhtT995nCmsHgRWEUMqcrYHcRv/FZ+GEG2/AyFvfChHJTOSgal2f1EPkqsAGcTD6uc+BjY+nfs9X17zOht8b6HvArgmjssat6/3JJRkVZg75ZlsicYx2fK7BeBLcN/0SDiZb+UERod7XdUn0gmJ0VyO3vaPUXpRHhlibU02YkrbxQek9L6IIRB073i5eHBz6tLRlO210u3lv7GtfL2wv+yOs192VMA5NPKJnr4RR/tK8OwlDZ6OEUddDO+g3b9ESgue5hpRV1CzsyLKSkMK6VpABd2bde4TQwgB8zAVUmTxDCHvMy1fChKGZX3HK4VLXUsJ4uPR//yPV3luz2ljchmY8cRGrMW+qb2Vnf6yFu8OBrWx/4X4Cnc8VRxRb8KXaKts9UXBNmkCtNf0xSpgwzA0w7P/bvyvta2r7GaWNbw11ugZXFtO33oZNL7gA+/9qdnVX4sy92U1xtTocxFsf+DO85YE/wyDpTHSyETkkpX4Jvf6S1scY1rOHz8yATZbXPezh5w/OAVAGjyVz7D7fgeABTtt3MV626e2YairlGOMQav7jqTXr+fVVRgkTu9UOEmb6lltm3RcRxxBIJwZy6oE3utdk/c5F8nebQ+XroiOBiKKuNRp76KGHpxa2EsZ+fSzgrlxR+FlbzdECj3ZXwjCLhEFSE4bTdPylrzWMSJEwlLVT5RRGJ7Zg8/BPAOQ873OUMGJ8osMhQUNk7MiYsz23nQ3PUsJQ7mIm6qlhngnokTA9HBEO+uMAZKD+ay9wsHuBnLAxkj/5WliTxEAEGfjV9QJol2LcZz+cFN46Y7hc3TJXDCyUwcGayt53C4iJSt3OCPJNhnde++MXJYTN4DplNaaPidW8ooP3mnCwg/NaHWKRNjEIQlduL/T6TXYb47aVliRhqpXOrDtu6tkUK1sAwBtKMpgWNFYYO7I8ssEdGTF1SQBgB1VKIbCMHZn+rkj9245jxJrkERxw5THoP3156ndCXkOLUwh9XHLqMgBJ0BEAjt99fnKevPxAgjOcqCPefKPK7veKSRIA8Gvp4+AUEEwaRKkzdN+6qUl2n/NLOP/ev8Vpj30eJ27+NqpF/WEMQSgX0wHTRdzKgwj+yhVo/t6vAgAeWktKVTkaOyaT+hbd7MjOWD4AHsr6NILV4BQpyxQJc+jJhkXCHH3GjGePJd/+TWy/7ePmz9sO3Nf5BV2fp0sGbMiKvettEubhtQTXnZO+r7LBvThTsDR01WSNd17TEeeo5ViRVdRPrN81nnq/0ZJBdioYnL5kElhvyslYK8eOLFb3XJb4zINNwpBKAKImqmunBjqCfVGYZAG7dUlIVEa3GXvJyOvDLbuTwECSuZ4ch6A6YgKSGi3Rj8mf3WAIjL5a5/2hiZv4iRvhLV0q34xlQGzD2Dh+5yv3Y+doAx71ELpJvw/+yyew508+nNpWoK75B1Y/DwCwF+oY2vFKZUfmTif3isicz3DXbmTBrLEgpMXWPLqgvN0+QIzJZr6ixN8nx2SiSLLxIRkw3PDj/Kx5bc1EbSXMXOzIwuJr58A/JPcgUSpR0Sq23Wo91qn68teuLWwPpJUwpTVhtKJolkoYYZEw+poqUyzp3yacmXulsCaMOqeRm/j329aMRTjQOIDPPPQZHGoe6tr2mQzbMms2SpgsCdMuIQbbETNBTI2woD3jAo6aH7Y8nfTg5i/mGTN1jKQla0KoHFx0DhZMHUoRIN6y5caOTNdRJdxFNZLztwOLnt/x/OAZJUxXqOtPJ/BQpBUgqW1rIlzE4NDjbhEJowrX5ihhAIA3O59b7U2bOt4rRJQmYUJ7etzOHz8O/vM/AwDGvvKVWf1E1patW12YdYo8DlgVYxs7x3MbzOlLSdkjq45dtG/frPo3X8jaOR74h3/4uf5+D93BhABIDN8iYdYN9AHcwSVb34gTDj8fX32/tLeVShilMlbruCX+ICJX1YRxax22xLve+9uz7ouIIzUnSLbBqYexL3+563dDRQptb84f0ccbDRz+ry9g08UvwuZLL0Xr8cfnbds99NDD/MJWwpStnY8EQ296U+rvMqtiPbevuI5RwhQ94xkXVgIpN8lonLqwloioRQNoTcvPHBaCWu4bP/KB0FGOCRklDGwljDU0T117bX7nM0l7tDxEAcYZXJ6sD13hHnMCrIf5QY+E6eGIINRk67QDFyP0KH56jlyBsYLArquY6FgVSOZITyKLUG/sx2U3/jYuu/G34Q7Pr8S5PphmuV0/vy/UJeZOYU5gFC62/ZHG8qEkqBKH8nOeVxNGHY9xOiQ/sya8JKdGTUwppn1ZtD30+xM7MpFWwjjEwSVWQVANvdhPasLkB8iJtU/P23154n2ZQ8KIOMbQeFIzo00G1W/wVABe76v5nnqgbBp9wmyfCI7hN74BAOAvSGcRhKKGiBNpjwakLOqKcM6mtlEg+ZV84kMXipZ9Vi+6XI+VevqaWT58fNe+pL7fhcjgA8Pon96FpQfuw7LR4uL2A696FYL2OADAE5JMKiKnbNDXX4kPv83BP76OzoqE6feTc9Hnl1tqLBusoLnz1xBNngm+5z2F7XzL0o1yTVAevYm0S5PtxiB41ZP/Y/5+/7nv7/yC2f/y326z4qBxbAXjti4h2LQyc19lJokR7yQNZqqL8+3ImMApo50ZME2ldNh2OK1smFRkC+ExqADa606UXRDy/awSJuY8sScUDCc+Ua44sEkYWglSpMChXWkSgbWSybev6tMEQsBX6q2Zah8ONJKsxiRz2lLCBIMdJMx0PIKxF16W2JHl3B8xVbaNW+8CNv5YvSn7sLsxgx+s34tv3LsTHvWSIolqTJ++8cbUtgKlhDmvprLSzem1VDzKjgwsIaSn3TSZcOD//t/OflpjgSsKaj9BXgcCxIzhAOBDYKLAjoy2Vc2eWRbFjAJ5bCiPTR2DMmKFZSy/spZ8qc8OJOeYVuSzkbe6Z9TaqD2/uJ6TEGIONWEU0W4pYWIuCmtk6PcF4eaacnLuX41cJUyRLZZaZMWWujBVWLMA7/npe/DpBz+NX/reL3Vt+0xGaNuRxayrVVSWw2+XkIjtdpRSoQFAq4CEiRhHXRGvTWUTRoWTW+BVWOOpIBwe9bBkam2qzd1bE8WbiCMzhoZq/OEiwO7B4jpk3rU/MK9PmEUsX5Pj2srWAUmpjGwkjyAOQfTY4uZfwxm7MFcIRG4yjxLNfNXdbJGtCXPr6VZAuMjWbI52puEclTC+SJ5pL9z2usJ2Io4RZzJhYzdRxbCJn68SRWQCOnzm6M5ND/MPzgUIYfCYtVaJBM5fMpRqJ7jIKGHkNX/P9A5MVCQx36gtQZnFYlfEzNgkmv7pNWy3hCU1rEazUS/OEgc/8Ukc+Pu/B1cKronvfX/ett1DDz3ML+zgf4vNryWWu3gRAGCzyq3rsJawoAmXJW3gwKNy3tUusB1llh2ZAMNURSeZeMhGvg7ukG4OlEepRIsFMUNbkTAs8/wXlhLGTlwhBU4qIjN+dquxFfEotX6g3EWUU7+0h6cfeiRMD0eEvnqyiD3+8Dkmi1MUBPd1QfNYKEsSoQsLzl62PN+LB9dP/7ZXoGoghMBVn0kSRr72cxQWA1YR+LitF+Qq0GkrYdT2uHkgWMdN6KJfVlCZEBOQ5NQzXptZOzJCCFyaTzr8y2so7j9Bq3jy93XiO98xr7f27zCkTV7RdBHHIAB8pcggkGRARGJ41oK4UwkjQeFZxAoDm5IPsIkb1qfaRbyOxvQ4hMkULp7gL9l/t/yOZQXh+sXBLXfxYtleX85dFvJBxQGz9mNh/6LS9h2/V2JHBgBvuPwM8/qfXkdBCgpsLv6jP4Iby2Amhd6/7gsf36vgiZUEzcrslDCzaaMxUPHAwyVo7X4rAr68sN3CVckExWT+zQMJYxOFb1qxNPXZBcs6icnZLlSbcXHQ2LaHETmby9qyxJDn37UOa6O+FFM/u75z25zDzQmkv+xkqXxqZyZqcZTYUC36nd8xhKqAHDcPbJ9KtZdjDzXfqTektc1UpWAMV5NrIhhopYKKIgEBIAzTgZ7YCpS5fTL4Hiw8CTeeLgkh5tRTGVOVhowo2kRi4AQddmRj44vA4jhRb1gE76KqvBd1wLHBB4GvvglCCFBFwrTV9j55w2Z4xLFmt2qfM8WjfaVUOGtCkgn9OlKZo4TZ76eP29K/+Avzmk93Kl1SyhZeYlczeihVSBwAfE4L7ciMxSNnCM9ZmtsmtflVkqxr+DEc3r0mTJbo4GVqEmvBs6UlC5OLnCz5PKxfqwLTRXWU0FkDppQ8UsFkz6Epi8yi4KtWU3DCEDlK7cTK7Mg0SckMAdatJkzsJEkbfa3yZwMAbBqTaoLR1uhT6rd9rGETY1wkVnJFIJnPWyV1h9rtOD0ZA9AsIG0YF6iqMantyXHUEyQ/o1IkNbaglDCVOF2Q/f4dY8kfMTNJSzq8z3hCwris1aHSpHMkNzQ5ZZQwQhTbkZljzjAwkxy/PGIzS5I4QkgLJF/ZDc+y/sve0QI7DovkuWjxuWhbY2tRbRnidL9/bLQz57CbEma4ucS8rsZ9hQFpNjWVmn8CwNalyd97P/zh7FeOLTIJHmymWHXZw/yCdRm3Uu0ySpi4LfCHV6Rt79rNGJwxU+9SkzD7o0lMVKT1Z6syAlKy5ukGEccdNjxc22V3qSmkLQkrEYC4OHlpLmjce2/q79aGnhKmhx6errDXdWVr5yOCGk+1DaoomQO3Yw4I4LwtEe788iYMMgIh8pXAjHNQvQYUDNeeo2xkg1pHjubEQTkHc3gEYiV+LmIMTXccAND0F2T6nShhQnsJQQtImIwdWTfVc8SiVD0xl3tosu7HvkxJ1MPPBz0SpocjQsXKZq+FA6DaP5bmX1K6uH0stB2ZbFcWIM+CFBRYP1JoWyQNPygmhHT2/l9e/FY8ulq+7g8GOtotHkgm0UMnykFQT5htJYy2I0sMuiwljIrmEivL+7yhU1L+4lwN6jFLkzAA4NjFxKyH1B2nemiqfXQLyK9o924sOPyI2l6c2MblEFSL3y/VBa6yGCJEkjAxIrglNWH04jXiHERfB/11TH4/yXCq08Q6JxY1jIqBpFZOie2WrpMy3p9Yjfkl15gOJqxVCduiCwnjuRQtK9Ya5NghlaGIVNFYNtKH3//1Pnzo7Q7uO7G4L87ggCFhko13J2FsJU43QggAfv2MX0ef14c3nfymrm1XDCfbnikJ4noW+bnaXwcACI5i0ajhWxOaRsYW0Xdytn90JWgAAJFFPmiZ8T0nWhvOBJBjpfxpECsYJwQO/+d/dmw7ZgJujk3Z8gE5DmaVELFVE6Zy4gkWCZMErZrTSTZvzJKgoRDc2FdlrX00iDpgRAiQas0osQCgnQl+x+2kL55SQARrLkZE9VgRpCyvYqqC0VZKu+8EHUqYJh9CHDGjcrPtAI8bOg4AcLgmrWIORevAnCrCZgyixrGWukeEAJo8tObX6pxlzpevbGSqO2WNnba5fztrwsRu+rjZdof2aw1b2ULKrKimpzoyU33hFCphzJgtYuw/80VYtue25LOcwKEmUWInhqOyp1oHigt+Z4OyrKjuCZAK2lyzRxbULqsJk/qqOtQsKrY0yPYlahcH33VBcocS+NY4XxR8bal7mxOGt0/K40FFiRLG1O7hIHrRVWRHpoPMlhKm3x0q3HYevr6xXLn2dAPnAo/cvBtj+7p7VWu1RkWd3m4B8g4lTJkdWaPzemoWXDcxF+bebKvC10K4+f1hzCJhBFziYv2yG83HAgQz1vNCxHGihFHzoJAHaHqSLJ9NHZFuuQtEz7U0iehOFSphkrc5RqaTsYXlqX4ydmF19TtcPdtnUz8CAG656cH8D/T94QCT0QwqVlC4aPwgBWuPImTJ1zx1k+kO4ylCbcfQY9h+OJ8Qmzm8H5GXKcxLEzuy1iOPzKmfR4tsIsjMTTf/XH//uYxsokyRMlLakTFjRQgAjZk2RjLK+9Z0lHKutddxbVdej/uXnI/glJOPuM+CxYVKmG7WvTNqiV5tA9i7vrTtrJFZkzXuuHN+tttDDz3MO44pCaOTj/WQUJZsE3MsbCfPaP067znPeBIMJ2CgSvnOiYt6Zhd2bhgHIJUw9pzDWXACIip/Q2RiK7YSpm1/VEQizdGOrN2OTCwNABzh4tFDj5Z+Z/y738WmCy7EzN13l2+8h2OKHgnTwxFh7bIkK6zlzSQ2HQULoaUjMsgWKRJGK2E8f25B7PmEl7Efq1WK++Krzw5XVmGHf6b8fg6RsXI4WWz1rUgGciCdfKktukSeFZI6NnbRr3XHXY5fmZaSSk5dkwUbc95BwlAra9qxpKEu9439k19AwlTOPNMUJfM5TezIcsgJbRNjyAAqA42MhHAtRVQ1S+CoXW7HzPSHZI5DjY6l/h7GlMVidQZA1vzmyRg5eRr9I3sBANP1RKFSRDgBADucCTZ2IWFOWtKPpqUNrdS6K0X+6Dcc/Oxsgnf9TnfV10DVxa4FPp5cXs4QEN+Hk5ngiFkoYSpW0K9m+ZQXYWl9KW5+0834yIUf6dp2uJYs3IqybQFphecpUvMPz5RF7wPv6EmYIiUbAPi0c/uGEDsKEY6dsa2VMA2LK84GQJi6htuiiR2DMqMvzmTNJtsW8HjnJLNPDVPZmiB0l6x7QwUDqANvo5yEvfKuvaZNc8oKrlmZ24Jwk71fJH3WAXwiGJy+PlRaibXO+FQ66zFuKpUWj+EGcuw/GE5h1cEkgNZ+NFEBhI48BvZVc/fkk8YGSqNFBrB3bMaMqbaScigYAgAcqkvFxeF4DcZjFw9tkhmiIW2l1EpPNvfDnPyC2y1QSpjq5X8hf1+Na8QKrnClhDmUsV3sv+yl5nXfS17Sse3Yrg/FSsaoKE6pZgDA5w4mi0gYvUucYd+i83HClkTdmFfrhWjbLcRJTZiJqY52Gln1SZkSZuBVv2Bet9VQyZqzW5jp5xmLi2X1cZQlYUoUPDypCcPB4KskhyIljLb3G2hwnLBH+UDzkvOkLPiIbUdWRFCZOj/28yN/HCjC/27+3zm1f6qx/vqduOkrG/G1j93TtW3EBc5rufjdySpe2fC6WkWRTHCwqMYLAIQ5902rRAmjBwdNZEB4BXZkwqieQQQIIRjtS+wkv3/cJZhu2SRMZDLONbkbsgAtZXsW+X3dYp4QEKUWQZqcilXfHdourAmj4wECDMJJruFcEoalSZhTFenO9Lx2lpaDp3709/L7YilhHh3bgBZPngPsUAFBPAcShnNhjoMp2ltC9LVijsAKkM94kxgvGH8nx/cbJUzVkc8eV4wczTTj6MC6zwufTdizeRxf/Mjt2Lb+qa+b1Yo4RpoTcNU8rqweEyEMtSix3I5bvGN92JwOwazEhgU1mQi4srrYkLdeeJR2d3lKGDo7JcyMWl7UWgLR4WJbxbmAjY3Py3Z66KGHY4+UHVk8v3ZkmsyIVZIvHxvrsNs0vx0xjIQJCfP6Q/J5kCXGARlH00tAAg5C1HyGuOhvpBeHxnaYhSAW2fLRl/6jtY7M2pJrVX1aCVOkWMzakV15X7FtMgC02ul1ssNdOAUqG429H/wQ+NQUdr272Dq+h2OPHgnTwxHhVy5/tXntsQDLJ08AAHAvP5o11C+DSJNcF28vDu7bWPfdJIDUdUU6R3hBmnRZuqg4KK1VM54ACJMLLC+nHoFvBeL2m1owimiw1SFaCZO1IxMCIxOqvb39wZVYxOUDjVMPoQqWxVyAquCSrhnsOj7G1a4QK4jrMd+w5V5BwfeF734XKi1JgCw6fJoJKuQpYTRc7ftJ5QIiJhFc67xWzDERqX/DOARVWdskE/l1SXqBezbZmViX5Shham/+CJacM4nBfhU48Babz+ZieSe6PLgCl6Yer7W+7iTiy1/2Tvz7VQ7G+rtLL6qeg9l0l1AKL0vCkO61cupWwP+RQ7PLyPSc2VuSzRaa1BRNeUzy7O7mCq9kLDnafSiy/omtwI0O8Nuer+NfT2erM9WI0xihK89fbBXnthFxDidn0RsQeb9llRA8tILAFgl64j6eWFVYdWEYF0Y1wUkSdMta+yQ7qHeMgw6NIAiTGij7DqevRZ0JTUUMryoHoyemd2J4Otl2vCeZgCYBwGR/mzzsUMI0MAKXJzUdbDuy1xz/GgDA4foeAMBYvBItUcHdj0plTMubxiE+kr9rOSzMJ19NMX1oEX72hcdAl70EADCsnFyqO3cm31UT4Gb2EnNd9L3scgAAybGu5BaxUlaUnYdRjhKGFiphuFXnx63XJEmuzm3U6lyAuKoOjEAMqoKdUVg8lmj1ibY4ytaIseH0ycDl7ktOA6XHgRMHB0d3FbZP7Yc6tXFUbGvSocop6YuuCcOrj+Cir1yEYFBm6hYFX6dU5tyyUYbazfJcUeGC5ajTgKTWDRE8IWGKrJ/UIktYij2O/HGgcH9mURvt6YStD8lFcF5QP4so5nipqvR+ZugW1mzR6FDClPxGq5Wo9DTp2GzlH8uIJWrdyJHjLoGDMNeOjKfsyADAIRxCXQuxW8GkTcJEScZ5S40/LVYxwVRBHIRx+WTAYcXPJiDJoNT3qiNoYYIEt+zIxtbFoGrumCU65ZsJSbKS+BhRz4xQ2YbxWRKtbqtTTSI4N3N9NocVapG/eh5sUm9QWQiXEX1TU214IhnDfVYpHH+bU2MIlRJmob8BAFBh9Q4i/eeFbCLIsx3f+fj9mDzUwo1f2fhUdwUzj2/Al6/9K/zLjf8CoKQwtJB2ZLYShsfoSBA6eLgJzpK5Sl+/tBt970m/YpJPIq+vMME69ZsFzzF7XDJ9KakJUzn7LADAt15I0FBWtvU2ELWLEznmgmjHjnnZTg899HBsIYToUMKErFjJbn9vVuAcnFC0K8eZRLz2E/lkb8Q4HHtdNyCtHfPmhoyLxI4MDBTK+h8udGyOs60ApBoR0EqYZM5x8sjJ+N3R8dy+GCUMJWBWrMMZ6nRIADrruAFA82BxUkG71UnClBFg9vZ5o1cj7qlEj4Tp4Yjguz62qvmix3wsmV4LAKi18pndigq6PsHXAAB2CalUoAU1ZMz3TjllHnqbD6+SXrQNDRQvkmwSRi9s3RwShhJqAok3qYLU+mEBa181McHVLNuJ5Wfx6OGkDkvm0Lh9csDm1EMrkgNstiYMAFDHw5hxskg28vyGAFWBVN/NJw9otYpKS2YavmzlGVZNmOKhQithhCMJNkYieKl9zV8cs5kDhlihlKDv0kuTfmRImAYfSix7shEXAFh5HvC+DVjwgtcCAGI/UcJ0cQBL/44oz9yoeA4W8ORY1AcrJa0lhoP8B20eCCEYmoW6BgDcOG3rwmdhR2arXy5deWlJy2OLvmF5r43tVf6qOffSXOFZkxuSCarn2ZFl22jkTYAONg7mtrVrwmj7pFHLRebAP3w8vW1FwjCrzgTLUenIbYtUTZhRNd5WlTXS7vFmKjumNSCJcMpjIFPzqeXKa2VsPMmSZDGDngKETmKhRFBe0I8KDmdE3l8LDj8MAJiZSU8CIzWxIzyG68vg8l9e/FegAhiY2CI/609+J+Gi0/f2RPVA6u8psRQ8DE1QwLazvGTlJQCAaX8MkdMEh4eJeDlYo22OQSgCvGBdQsQI83ud18KDxxHcd+06bLhzHx65Xo5x5z2RU9RxUpJRNKMZD1mUaz8oVO0eO9DhlNlcRVFHZmpAQtywMf+a1M8bKhjo8AgIAFdNyNvNnGCzunBH+5Kg9HRJ7YnwoLz+qsoWqKjuCZDs64bwRKwK/wBPnPBLhTUdAKBylgzo/McrqXmexXHxIi7722UFgbUSpjH8WVkwdPGXABTbELUMqclNQNoRLtosnxTSdTAkCaOULkXHhmmrs+Q+rU/kN9XILlTjHJXc0xlFll95yNaAmZ4pX8hn1XtldmSRIkIoj43qt1VAwjAuDLseWaq8KO+a4RYJo+Z1LiHQFV+Gll6FpQ/sT5rH3NhWtNV10GIVcMoBPq3+TsYFfS/ZcLuQMIkdmewDFdTY8v3/7H13nCVHdfWpju9Nnp3NeRV2lXNGESEkliSiccSAbWzANtgftkk2YGMEBoxtMME2JpkMQgRJoIiQtMraVdqc0+zu5JmXuruqvj8qdHV8s2JXAeb+ftLOe69ev+ru6qpb99xzbuZc9VrG4BAOSwZPopxrqUDEyAb28ABzpRSkqq2XliMrAwLqgwcSr831d8HEqfjTNf+GP177MYz2HV/OJmmTQGOamQ2rQJgyJszIweR86NMqfrFla27b5tSYZsL02IcQyazadJ2YZ8xyrv20g17PYytbl0x7/M49+N7HHkJ9on2w8HCtcdONAIBjJgQbuQjoE4Q7CocZiSkhgBRQMjUR6HWMg8KS+zjX69QMOhALAWufHFarjea+z2lcq0r3r4QJ4/QLf+pgH0FDdr8aoHCd/FXNnjP7qBx3uhYeONi+0YzN2G+gUU4T7D0OjgP1AyXfAPaM1nHBR2/Df9w2DeYcZ3j4zL/G5Py/wp2XCWC7aN2njMPOCXHnrfOUGWl4nGomDAjRPnqH9O8bU4oVH2YkUCuzlQxkMRMGAHbOSb6f7VDWH61NFoPazRQIY3GnVArumZZEnbFimwFhZuxp24TcyLhGMcEiOSdHSn8N8QFc2/owbqIXAmgPwpjmLl78NHuabz2zkwF0v1rsuDpS69rjcejWdbPn6lgOmIwIVDiw9Ioh7FosGRxGkNiXwIQtN8Se8leDZgx8pDZPdlM4f9Rkwpg1YeThbdvDoV75wri8T3ndsKRz7uVkZgNCUsOTdPawwWI5soIsw47zz4edQtxDK4RdAsKo/V8YNjQoZBGCWW/6QwCAu2Qx7FQgeGPjCkCxZopkt3oWwK2PJN4iLGpbh8W03sHyoqVzupNAnV/CEFL22pWvxfnzz8ffnvu30+zF9DbIVir4EvD2mx7XcvHZKz+L95z3Hly25MiDMH9yqQjG/NHFK0rbDSwSiMKh3cKxOBJMGK8EKPQPIwu1+WRSS/Vd11P82W35lN08ObIfn1/cD6pBmChmEhT0LaRMy1g8sJJgTMZw+r14fOwfj589szB42jlUgE+jEY8RbgC+X7lSsFaAEv1ZVauKM9AJcd9sWXy1Vks+r2Fd9MtiERxPnN+83mWwODAw8pT8bjwHxWTw5I/zVHR1N85GdeSgnpc8PwlOfLrzFDHnyXnlF+N/DtoQDmrTrYFzF/0GyKmPnjNHEMTrw1N3i43EhsXZdqNf/RoAwGklP6sHpmMcnwedEnOMed9La41E2cxUj1YSko+J9vI5sFgEa/YcBG7MVgzygs1yCAdOiIYn7mOrVQzETfzilwAAN2zPhFG1I0jlxQCAvYsugxUUH1uN2/HOmAlTWhMmTAXr6yXSZXkML6uZK08AAC0Z9CacahAGAJpBAQgjZekIp1rajxUcW2XGmdrR7TLl05uq5xsIM27cm3Y1XtKyPfWpdsBw6vtBcQA2lMw1i4UahCliwkSM63pSigkDAGEeMGHKkckOOSBgkOPFctA34WkmkBmPbcrfmArFnMO48MEaYTxXBbuzDDKbAQErfj7sdE0YTgprwqiEAg6KSXTpOSMPuOUaRBRzkKqdpbqbliOLDsaBy7UrkvPWji99LdWRSPaD4IXb/1icR6OCR894J+64/LOglofAYCIqI/bhM2Fsi6BTMuLLxuTEaPKZd6mPDfUbc9sGU5O6JkwnGUfTFQkQ6Toxz5TxvPv9G1CQN13zs8ju+tYmHNg+gS0PlwcLn46lJWSKxpiQI4tgG8WVeUjAU3ur+mQASlUCXwBLJly4TgeYxcC5yJaeiNoDfmN33pr7Po/CRL06wKgJkzNuVHFsRoA+uTfzIqB1hKWIlHVf8cKjctzp2PB//ze2XHYZhj7/hWetDzM2Y0fC2vlgpjWmAtz93c0Y3lseHzF9Ukf6Hu381H+/bTMOTLTwyVs2te0HpwyTPcsT75GCfb8AYbJ7pPyaMDEThoDBIrFPpfZe3fWkD2CxEJad2reF+dcnZsKI11V5eLVnyLSXPtB9q+L+TwXF82kgE5yY3Mw5zC0FYdKJpr8JSRnPVTssEObDH/4w6jPUpRmTtny+1D0O49Tv4RX5jq8CMVxOsJYfhzecL4K1ZBrB1yX/9UX0ve61GHjzm37VLies0pkEIrwSEGZIBotXhTG27uVk7w9UB0Ali+PGjl50zguwY4mU9DJBGPndyU4pS0YIOOfgURhLgKUmSpuJzRyzHOwcEWmzCSaMvPS25eIbl2f75lJPgx6+V8C24EyDMLWJOPBRBMLMeedfxnJk0vqn6gk5Ml0TRk30KjAQxcXECOEgMquL2DZskgZhLgVRUe48Jow6Ry/ZT8IPL7utq1G+GLm2hRv6xX35eTXQ97HMOtwO/PfV/43fO+n3ptWHFy8TAcv5nfPLj3vuuYnXbBpyZIBgDPzOib8zrbaHa+9dfSKe+NDVeP/LTipt1ztHsCMmhqRsVRtZwulYEeMKyGfCcOTLUKW15S/cwLF5dHN+UXPDiVLDs+EXz2lMPn/UimImTEHwNWIcHpesCStmuhFOMSALtv5iU8yGUCCMxaLMOShZL1NmijWbeq5Z5U1pJoxVwBDSJ8iZflbVs//Lp5LZgVFLSTNFcIxAs8cIbBk05GOxk9i9X1K8p6GjMf+Jp/TfTqqWUOOYy2RXpUQjHcBT20XGZ8OZAriNS1fOMb7Bjf8nzWGpoBkH7jolvjatDRsSHy/cn7xuE80mcovNqHogBrvF4m4hbZ+FNDNGXOYhYhxjOUXGVU2xA8d68F0L1I7ZimEjT0ZJ/BNaFL01OSYHi3XlqXT4bTp9JoxppATgaaxdCwCIrDipgJYwYdJMk8IaLIiZMG7k49xdqzFQWwSn66lCJkwgnxXBbDFAmFZ+f2hLMWe4fpZoEQMghwnDLKd0M0Q5LX39XLfxqfi67R4p30eE6aLpbVg0WnZLDuZSJkxLgc2x5FaroCZMRGMmzHu+3YzlElmORjdniZowAODCAifJ8bLjLiGDR2VaJAfDsjl9AICpSIAwnIuA8ERgALWV7DrhMODuvXcXnuscLvxEBfj7nBfKkVENfjAw2LrmXJDLnouDClf58+G6Yj0ncryH+/YlmzfEXDFVAXbOS3yEb9y/KxEQUgGCJ05+S24/h2ediOZT67MfHAYIo37Pdyztw5XKkY3KuU4GmR3qYX39pty25K77NeDSZU2g5ol7ECw7eqz+Mgt27cy++RsAwhyu1caOPHMjSu3hisYYZVwwYWgShEGU7NOOvZOIuJo3Al031PU65XsChGmw9klHaw4WFGSOaIadrZgwueuTYuYQ4IyOAQCAFwIhOzLXs3rWWYnXRTUgngk7+IlPAgAOffrTz1ofZmzGflW7/tE9OPHvb8aP1u1r3xjADz/1KNbdthvf+sfyIu5mPZiqkihtI0d2//aR0s9Na6VkkjlIYS24KCExZhwjJzGKMo6KnFfmD9NEErACoO1UnMuiISpOcp4lY9nkEPEDSSbMXMl6H/r853ObK6ZxrSL2QgDw9Se+mH9sAEFT9DeQihc2d9AIy0CY5DWo33dfYdsZO7p2WJGvD33oQ5iaKkdCZ+w3xzyZQTY/FBQ8SiJUKgO5bVVwvAsErzh9ITwrlqFqZ12XXIIF//iPsDraFxI/HFN1KfTrEhBGUdWPiWw9sefVhAGAyBYT3E67B/ttGxTCoU2DMIQANf2bBAhDIApiJszyJPPHkZt5Zrl4QC5cUY4cme14GJeXygwyO8zTsjfVAhDGqlTgKxBmMl7wimrC2N3dmQChHbUS0lAq0zC9HoY00KAQsS29ieaUZUCYKTYXgOgzKUzVBxzf1QEwACCsPBtrzjv/svTzPBvrsvAvfQ2s82kuEPer2tvPeDveccY78K2Xfqu0Xf3BB9E/EgcjmPXc2Fh3+e2lECpd4l6qQvFHgglT8ZK/u9zImHFT8go0ZNi09SV4+Ky/zgAxuRm1nGMqJ8tl0YO/iJvIf5fmBJ6VqWR8ZkVaIiYqAmEog8dj7X2VRfPYgbW6KLAZhFfxXsIpYNmwFon5Y/1igEoQJjBBmIjquaazZ4HO3veYW5AlFTNhnLmi5pJyTO1U86ilWAQhHBLfl1aFaJmb3kdirW8VvOyZjK/dK455We51qXXEUTwvNY/Z/ULu8oaT/12/N0ui04IJY+N8KUfW3P+qzLmZ5rDkevPGhz6C0w/8lr7PU7ffnvg8cFIFHB/67/iFEbxQDnCCCQMXw438gtOcRjooogKjHhXf3TmcF8yWjEGLw3dsVJvQbMVdwzn1WOQczOwItqoPUysOYmspOKqYMIcHwrCgfdbVwpHpMWFYioVT1hfFhFk5dC7O3ns1XvfY36C64PuFTJggNOXI4jatIhCmVpPtYyYMLaoBMC52YYplAcjrWjp3JM/t+caEsY1bdXCyPECXZsK0SpgtjMY8ulD+0SypaRSZcmRyvLcKQB5RR0j6KJzCpuL+OJxkA6qGHBmRiSIOMZgw0rZ/5LPi2NJhoyRAd1WALw0qwAzOxVoTmkVRcoINNkUC5E6bp2pB2Yql5RQWBw/2HdDnuQsLYMuASm4NHwXCEMCzXHiRaDtnl/gdFaxUxiV7LHCA6y9MngeHhZvX7EZNrhmcUjQqAzg050xxzHMc0DfGwPsTp/wJtr37A9ku1WJ51mg0X2pJ2ZS8352+o5M3yjKDp8bFvNslZUkrkQdWAIJ23PEwQkcmqJEpjFdEYPzJM48r7dPRssEP/H3mvZnMV2HcYEdaR8GXp2FyXmlXE8YxmDD1ZgOMJteDRi2MmTAk0DUJPAn6MQgfYpIlmTCBnCIO9sbv+QXMLB5FGnRp2cK/0IzNXBBGsjoJYFfE/OUwoBlMry5UO+u64vLE6/Ef/ADRUHF9hKNlv2m1lWbs19fe9e114Bz4i28+Oq32I/tq7Rsh6ZN2cJZ5L89md01fpWJiIiXBbrmFevOMcVRykg2KmDAVFjPfzXBEJOdJOyWvaLMAnpOSo1d5iqnjcyNpJdGXdY/l913uA5gFqAoP9+69LbctECsXhE68H6w3i+ffwQ9/OPE62DlTd+vZssPyOmYctxkzTcl3kUkRrGo6NXhu/oRqSzmyHkrwb284A0w6v0ci+Pp0jVgkCYyUgDBnv0QE99a7kc669HLkyAAgcsSE6NEKmhYB5eKamKwfQgh8J9a95xAFmLkJwvR2J46r0HlmuXHhUsagYu9MAlq25eYWNHWpD0tmSVf8LDMAAKrnnAM3lIVhjbWqiAlDPA9jvcnNJechHCNg0KOuK0/8A0oD3R/LsXR2PSiFjRwJDC7GGS8BYaqnnKyLSwMAaSPR5R93+BtjE3iZDhPmcK2v0oe3nv5WDFTzAU3Tlu6OF+ZOduIR78vRsrRUxBEBYQygcI7dhf+95n/xrZd9C7e89pZM25H9NbSCXkz0rEDkpIq1pynGAE7eyXOdSadm6LQSlaFYbIoJE5FIy9sUy5FxuMwAYeQl2j6yFb93/lIAycxKFSe2WARiEXS+9nUAhFY3lfWCIiPgzFicue3NXqzZEgBw4EAWcLKoAmEoel91rTh/mal5Ot2E6Ob36rZUSzlFsA3N3p+82NcBarMouZrzhgbia//eC96PDwxls6R2zjtWfAcUfooJo8C24c69UDpbywJRq6rp1ABuY05XBa84fSE482LGTM701t9ckHhdjbpw7OgLcGDu2eJ6VJOF1MPUMdY8/h/5GwQagYMkJD8seJho5TvNLAw1JV4B5K4EYfKLQysQBvDlGuVQcewDo9ngBZduoG1RWHKjkZYjSRx9wSLxr5LELANhcrJWW6T9vFZtxUkFPEcfWVmaCVMGwlC5EVoyFs+Tpxy4qJAJ44yLDHLCKQg4iNyktQpAIarqIHGmZa6Krs3wfwmAjhsAJSNOaZZvGoQpKqz8XDViBDyLgC/9eeqeFNVsAQBqsJ9kab3c4qvKrAOD4l8WaiZMkXwZZRxc+Sg80sCEg2xAlbMsE8aBBT8lX1evChA5UmuBFaC76stjJNcCU189j71gs/KaMApgVSCMza1CJky0TWRxVlsUdVTLa8LQOPDqWQ6c8YIMUGl0QsxbDoUu3q2sv/d07P3GNnz5L3+JMKDgYYiRWeIZnfJGsfIVPTh50Yl4bOCz+js7l16V+Q3WiOdPXiJ5CAATkt3TXXFQkb5ts2Tu2HVQzJt+U4A7LvXASmqnRY5YGzyrhklfBMb3N8U9sLq7C7/3jNmvKRPGBFVy5SdTZspz2kdhL0pT81xhTRjGQVI1YTyWrTd5cKiuJW0ZCWDJ+cZ1xPcYF/5SwFMqD/I0h42h57n59Sy5IX/akkE9VSMmL6kChhyZmRQTfunIZFe3NmZliva+811H5NiHY4rNN2Mz9ptkUw89Mu22igljcw5fKZ6w8rXYtHYxZpp6BqntYvTr/5fbNmIcFw7GSap+U8yNuTVhOEfTF77SzjkUfo576qTkFS0WwrdSe/cFpxd0PMmEaWeKlUxJvA9ySpbssKWSbWKQqlEgmwwAwbZt0+vIjB11O+wI4uHUV5ixX2+b0y82Gv2R+LfB/UKd+p6BOGDFIq43ceQwasIcDTPlS9xKsZxBV5+YbG2jnLdXwA6JbLHouLQisj/lJt5OSS51+Q70vEossKlJyYSR7VPAh32KyAyf7FkW/xalcOSCoWiLtuVoaTIzw3vJ2AmwZLSxWslnwhBCUF2yIPN+EROGuC5W7Php4r3xCkuMA8WM0FdaLrRBFCZBGM2EobA7jZQt/TUZvLOLF+rK6acnMhbagTA0xey7+9pjS9sDyTocR4MJM12b++53o8MoenfaqufPxtrx0iDMkZAji4/xviXXYHZ1Nk4eODlX1u3u78ZFAJmVYtjlBLU6WvmFRvNCiaxAzoszpuWHqEXb1oSJWMyEYQYTpmJ58F0bnQzYPRQ7XarbhFPAcWDLDbFgykmwwZB2YjTO3PbcjoSs4OhYNtDsqAxMh8Dp7xfHlsF9u7kCO+58FOGoYFpQGbgjLIJF4vsyPuBoppopxRTK+aVlBOg63U68fjILBvU5CuyKMgynixddHH/fThaur3vjALeFRBfnAIgR5Mzesyu2/m7mPQA4NPsM8YdlJTYNjRRu8dHZMbhntuOMZWq8ELgYq+cXXuSUgskx4ko2lstcgAMjuQXLJQhjEw0Se4E4dpRHZJZAALOpDjKHfn6ARrRL0vPLpKJ4Kxu44AWBKDPT9I7TiN580KiEHZI6Vvq1aaoguWJfAsC88VWFgEBkMCAAGNJVBTVhDPkyBcLQgraVk08W3zEASm6VgzBp+TGGZ3e+DyKG+7cNT1tb3FQRLQK+lLUiCmoAEGXyYuY9V2BqWfuBe+8EIJkw8j4FhUyYuCYMYVS3t3lO0J5zgwkj9dDBE0khADDZvQQAQLlaCwJ0+T5cm4CnakM1wvj54TlBZYcCIc1/PsScEwM9gBj7RTVhuCfmpHqFISSenguiQztyGqeYMG3qGR346HUAgJ4cnLnqxYW2H7ppB0Apah1izd4y8Ah828eK3hUYqW7Eiu3C1zww95zMcYhR55A3yuXumvKZ7/BsDVS3SsbxnqnHAQCeTFCyuLhWzSAEHRvLtI9khqxH6pj0RfDHbfXIzj0HkhmfRyBMrRXhge0jWk6yzEzQu14yByjLq3d0JI0aYGBn0GjDhKGJmjA2dTJjxY04ImYwYRQII/0gFeQrGmJ9RiJ5s6C2JqcGE8YRD6x6PX799Zn2tXvXiO8RwB3bot8na/cfkcTdiZ/8JPNe/aGHfuXjHq7xEkbujM3Yr6vt+rss67TIVKKiyzlcFedpI0dmWq3NnM1S+wlmuRj9v3wQhjKOuc14T6V8szwgnDIOIv2x4R6G5TmCATUv6ctbLISfTjpfeoH4NxUn5wZz2LSO88/P7TuT6wa1DYWbkkujJHYjpw7IBOZWY2a+ej7YYUe+rrzySpx11lml/83Yb4YduzCZ0dXiLqwCkK7aY2TIBDRmwjzLIIxpZQCjLYOEXU5cE6Yocyp0xEJRiToQzT4ekSzwbKVkjnqqLrilAooEjbXrwMMATC4WadDGqxoOOvGxd6wBErVQkX5+4KrP7JgJY5zTSQdfoEGPagFjCQBsz9VZv8qKip8Rx0G1kQx2Pri8I3FfO2TAncigyrrmQWwb2wZKQxAZELNsSzMQOKWwG/uL+2cVr0aEEC2pAwCMl1Pi3QULE6+DjnxwyrQXnjBX//1sgjDeihWotkZw5tpP4/jN38WppxUHTp9r5nrJ62Y7v/o8YDJhjj3htaVt920e039HdlK6Ia2XKtoAzZxCow0/WwB1KPWsKAcMlOqgKzXkyBQI00xl/EWU4zjsFX9bwDEigRunDFfh1QK8baKKlffEm161v7cYheX7cCXbrbfWr8EGti/OaqJhpGUmKlLqUWULjefIBcU1meKNtZakgo+fjb0bP7hro/gdeQ1JitFGCNNBbW4GoOWcx9OP0+pPZPrRoALcYIgytX4qTkXL87xg4COJzyb9UQAOfMfCpcfPlrUeikGYIjs0V/g4bHxCF5EGgCeW5Rwjb02JolzgbcyQ0zGNhbEcmQJhCCxYYLhnS54sR1yDrOLa+MFL5qLSFNnY4XiyJY8iDfpzm2HPYgnClKwPwaCY7xXYHYTFgaw8WaAiOSQTgKhXjIBSGTCRqgFTVp9GB/G4cU+CgULWhGLZEK7+Ff1oFtS0CcOWbq8AG1rPl+91l4hAvAnCsDYgTJrxMFAg/fpM2ed/sRW/9cX7cN1NG9o3RmLaQLNkzABAM6CJoqpByeacGpJ0yp8qax+p54OFMQhTwISJKAdXPgqn+r46INmAqgGyK7r0Pt4CJcm5dKz3WDSnQlDZj8gK4DsOeqsuIIOwar42AWzkMJ8cJmRd84wyrlOGtBwZSkCY+cKvCR2KCK4Gj8J61ofihgSRYzlwO+dm2pjWWp9Tw0VaTy1OJHnkpp2ojTU1CDNWPQjf8dHv94MRYP6B+8W55cyfjYcf1n+brJg8U2wgz7Y0E6asJkwrEHOzF4jn2eYewIE9b38HNl1wIRqPP6HbUtcB1UyYOiYrYk3tn3h25MjyLA/Qe67ae69/HK//whp844H2kinUeI7ZNACAwKiRFrUBhp+OMSMbuTusF4LPjHE43Y8n5Mgs5mTygViLgaq6giQAkb6OAmE4KT9nDqBelRnqRXI1YZYJoyRIw33ZfZk9W4CofghUwhT4+TyQ8Ar378eeP/9z1O4vr3fRjl03YzP2fDH7MOJurUPTr9kSGkxhlyffKzy+sd/OZ/bHRpvJz5lVzNinnGN3zwKjrZgj84DwiHKofRMlFF5ON9L1hW0awneSagikSBpWMWGkO3fvieL6d178gvzmYayAoUGYaTBhmNWCJdkwQV79zwKLRvKlsGfs6NthRxCvvvpqvPKVryz9b8Z+M2zlmcmNV0CKg9K2benAfBSw54Qc2eGYI4PGx/R3oE+ySEhB30dcETjtavUjfP1XNNDguskJurfqxrmehIA1G0AYy5HZKdBmzux4EbDh4Kl9E+C0BV8uGC0Fwlh2Lu0xBIMtQZiOSvHiRXw/AWRYNMgASHFHbF1oW1nTS64WVS/FhAHB39/8R3AntsUgjGOBKOZPFCUKo41WB5P9s8sXaieKNwIc5RTyjvOSxe35NBgZVSPY7x0BBsfTNXW9+sc2Y8neO+H1Z9lDz1VzMnJkR4YJo4JX3d1JNlczpLj+0T14ZNdoJjuP2ilt7Bz5o8kqyWXCjPTNybzXSmn3RwdF0XpOqXYEIyvSgTEVUAp27Eh8L6RJJkxVPmY9370dq4b2ymPGWcRBpOTCIvEMy8zgoDUbHfKcW/XYoTaLhs/rEkGvVkUAHLvujwNj2lT9JmOjb6eAqQP7RJBK0anBk9fyrwZ3JeTIwtv+U34ir1l63updjLQ1fdFHh4YZJgwAXYOmw9uH1w68W78/0rEPnu2CEIIXnTgv58eSRmVdqkWr+jOfcRDQifFEsO+H5+cdTwX+jUAxpfqec4OpN1HLD4qY8iBuEAf1XUQYrSfnQs44IAPBxLGF5CWxUJHg2sjGpGwQDwIdYKE2Q688PomKpTk1U1Nm4E80xorbNlRBa6N2EfLXEm4E5iOD+aXAjTxL11zhtDgIJeRpqMFlFSOgiAmj5MsUCMMloFhv5vdHjfkECJOncwch68IBzUICpBxZCTiRBmHOnXtmYdtnwv71VgEaf/neHdP7gvEMNFvlAc9GSn6sHISR2uPg2icrYzUooMRisbxYnuQWIGvCqHmPUy3R5fAiOTI5aE3J1NTmHcTC6GANEY9BmIrjoMNzwHl6PsuXIxuT2L9NgTDMB28jxhFKZk0ka4JZ3C6UI2Mq05tQ1EiHfr5D5CSmGHJkDnHgnvSqbJsC60IFj50b31+C5DP7o6/sxnjvMQDEnG3BQo/Xg4kOc35NZZqmwMt20kEKiHJtSzNoyxhdVSbWAC9QtZwsuMxH+Mu7AACjX/+6bluf26vXeZ/U0HTjJKXQ6XhuMGHKJOyeY3bDWlE8+sv3bG/bNjLv4TQuc2AwYWh05O+LMxgXvmYgJWsNh9vzZAKEAbL1mHwQPW8wEuCBHWNoRVQno7RU0kfJqXQ0RJu5t63L/dz0U9MgTK40qJyXdswjqKbGdllSwXTsmajDMvjBD2Hyllux641vLO9LWlbykenV05ixGXuu2XRYhcrKgI60mUwYb5pyZBPGHNwoqeUHJEF2IJZJzG3LOM4Y2qFfqzmsmTMHU85B5B6UWRT7ljHMHkrWa7FSSTAWC+A7yWQQQgr8fYMJc+rsU2PVhIKEmPCQ8BmoFeeMleHrkbwux+8IYMsahK3m9JkwQ//+H9NuO2NH1g478vXud78b//AP/1D634z9ZlhPZ3IC7GekNCitgIwooDpY8mzL2519jZD2Ov7ceaXtlHxSj+fg5AVCVsDKKZQKAJOyEGd/Yx4iFsGS0aS0vFhPJQZhOIgIzERhHORKXUvL9eBKWZnVm3bg5icGQaIYhGnK22ERK7c+xQ6XA4EIYPp+AagCoPnYYwlgxWYBUADCEM/PFCwLUuKVHSkpMwKgPrkX83b/RC98lmNrOTI6NoYmj1lWNS+Zvm2VpQQA8Ftj+m+OcjkyQgjcRYvivlba63Uv6IsZJ8/q+E3dE7vnOaA1Pk1zM3JkR4AJ49iFe8/vPrQb7/r2Orz6P+/F8GgyQBN4SbmyvI2fzfPlyCCDpk8uLe6XBmEiqh3a0G4hSsmRkRRIGzGOinwO0zWe9jk79N8K0A41dYCC2LYG6c4e3IQO6QRO8tgh5kbQodqVBPAmNqQoEzCYMEaA0ZQwA4BQUqC1RFBKQunskCKUAuWM2Bj8548DUQDtiqSnVD87pgNPzL+Bk2XCAHH2OAPBPHcLlnTeiPuP+Sqabg02xDUe6PLxwhPm68xRDoL+3/0dfQyRQy6O88I/OAHX/tWZeMvCP9Kf759/AdjEBJiUM2Sui5aXXQ/ysnFZo5kAYVRgd7KeL6HDoxi0caO6ZhI5FsWGwYnksQ0QgjgOOj0HTc/Sc2LXsJvYFAkQRmbRuhRzd8qMKtsrlBLRNcvk2AxyGGJx3yMI4bf4eXeYm18425Adoza0zGbl/7JyJMqm7hFSKERu/niJzA5lDCDJ8RhaUWF2csyEEd9xZUCpERQwD8JIt1cMC5auxCmNh2GiJhIgN4ll0mupwCkvue7PhP0qseRWiYQdkJUJKmKqADH7iSJmVoclWe1UAl9CjkzKOBSNAcYBCWQIOTLFhMmRVONcs/uSiabZdWP0QF2z20K7iYrjCDAgFYRlxnhW7IWpimBmAiI7MojywVvKuK73pEAYwi0EBUAla8ki3IQBVsyEaTZzxqSSI7MAJ+iBbU+flXVV9Vzcd75xXhJQqdoC+J8ci8BsH15rFAe7dqHL64Jt2ZiYU0FRdDn93LM2cmQKhPEcSzNoy0AYEoh1yA1rMRA3EdeWGr/hhvi3o/i5dkkTwx3xXFevljOGnjF7HjAU0jad+SY0wFRSAsgrayVAmCMPTHVtjBlSnJDitUZ2NQ3CREHyHDwGhJoJE+Lzd+3EDx/dC2+agdIeA7yc/1B+XQAeRdpPjVwxd3HLBQfB2Le+nf2C3KdSC6gcBgizd2ovzvn6Obhhyw2FbXjBWnskLRwcbN8IwIHrPpZ4vfN3fqeg5YzN2K+PpaWTy0wzYTiHg2mCMIZ/0Sjx84BsMg6zy0EY83MNLOfMwZRxWFDgNsPEPJqJa1Ua+7Cz70n92mJhtq5WQUzQZML0+r1w5bR46NOfzm2u9pUtl+iEasKL6/9F0p/prkVwlDRZAQt/xp5bdlggzLMdMJ+x55alx0Mfs0rlmRSQkZAje5aZMOe9fAVe+3fn4EV/WF7UXAFIYYvqzTApONX+AbHR6mz14Wc7fgZLBi+dVNC815AXAyFoPvkkEIUGEyb1A7angzsOLGw5NAUSNXGszIY+c9mFieYbFgNmxqDHoSU+XLf80bcSTJgQpGBxsbs6Qd7yJ4n3Qjd5T6s64G4Ebzkw1rksrgnj2oATB6F7WZyJX3eTgUbbLV+oe06KZR8IbU+lrZxyiv77qu5zS1oKe/MLVuAN5y7Bv/5WQRG2Z8hIqoC81ZmVxnqumpsCAZ2cAPbhWsUrBhYHJ+KA5aYtyTHRrCxJNm5ka3NYLB+EUZtMmqJ3378qfq1BHRozGgInzDBh0hZShvPIBnn85GeBE5/PE/u2ijYy4Mtl8YXJ2+/QbRTcG/gx2BIZGUGVjnIWFTcyhUwmjJOSn6CSTh5vvlPBnmo/glmB7KeNsOYALNTBQp6myVd6cffy7yEkTSza+wsAQOgK5lLLjXKDD7YMhKoi3ad2/ghbZj8IAFjcH4M6x842AR6Cuf/vr/WrwOuGxW0QIuqBLVrZj8rr/k1/vmPZNWATE7qmFK0IWjpJBSFYQ4414+1o6JBxz1t6Tq818gFjbowbq9LQc7MHqmu+KDML1du2jWUDHdg2l8CTMmah24mHBmM9ddYKwFRQ2gH2zJXSZMROADqJ/qj1SfbDYiWsGakvb4Iw1HIx+rWvZdsaARtGgLq8RPuyRCRtzR27E31BSSA1okp3P+5vy24VZiezFBNGMbgm9+VvKhUrJ1ETJsNsEMbDIAPCHG5NmGh3uXzK0bYFvYcnf2k+2c022t8ktVEOy5gwoRinlAC2fB6CknHQ7JBJNIYcWdHxI8Z1oRnLANccnsO2YUzfU+Ub/s74JMarWUBgbHAKkSMkIFt2A77jSIZtckwMGjXfoGtpQdf8cxhAC+TIIkOOjBpMmCI5MmYraQ0Kz69oJkwrJxDKGUNk+6gt+Azc216HXV8urwnTedmlAESygu9UcGt3B979ZnkSch9xIf0K9nTEtdq6avsBAsyuCrZnnZaAjikQZvKWW0r7Y8qRqTm0TI7M0xJ2gWZa+zR7zjvGd6CukhtIA5Zciw92Cimt0Ot6TjBhag88u3PH07HpSP82m8YzOY1sb5MJs/up6cvuPB3jpJgJo/bDNkv6M2puU1YBQSjXL0paYCDYPdLQCYE6qaTg1EdWlj+nAMCjUPsb1a64vSmdmTB5TtQCqqnn8NCnPlWYzHHN969Bi7bw/nveX9iX6EAOI/sIG/Hia16WxDF1221HvS8zNmPPtLWr28QMtgkpqccLAMMNIWt1yHG0HFm7mjBm7a5Gm/qCjSBbE6bIKONJpgyxAMbzmTAs3t8uj1roNGJtyryI45aVXzbeIfDdZD3ZOCaargkT+24Vu4JLn2xzzeU8NNYZyzJbKAa0pppif2cxClvKkPECFn6ekWq1faMZOyp2WJGv9MM6NDSE4eEZLbkZE9YgPBMUMk1JEEUB0wEe8izXhLFsC/OW97SVQ+roFo5aYzJoW8/mmmNfDwBwmY//ffJ/NQjjuskFo6fqJJgwY9/6NnhULEcG20UgaS/McjEyVgOJmnDkPsJKTaR//3u23qwDwLIormfTDvyyjcneKmHCAEDPb78u8TpKMVV8x8pokLqcY7jneMBkwhig3jwr1n+e8OM5xqIBiFu+uHTM6dF/H7Mvm9GftgUf+mD8u1e/rG37eT0VXPea0/CqM7NSSc+kkRSz6vkEwnT0Jjeb6vn6VaziFI8LMwPx0QfSmW/JZ39yfTbz3mYcb/7Zm1FLSb+ooGmUmj4ScoDyIY8OHjTkyLIgTDrrL6JcfzcNwjhevBZ/7/6bRRtVsNWS52r2QQbYIqOYNzOkjypVAUio2iF8UXIuiRg35Mji9/1gLNHunKm7RQRASTPlgDCTq0TmNrds2D4DaAh1DzLrgdeJSys/xpfO/zvMGhGAFJMBAkrymTBKjozKCzBAJhDKw16+Kpapu/aEy7V8HQCQH71N/93y+wAAHb1+vDYsOR/Ds4T8TM/kDlCDCRPJbsyhFC7i55A2s4wP3gr0PffQ0KzDRoGEDo8iUEu0d3jTAOIZJlKyTSZwMvuR7SCEYLFzELYMYFLbT2RT8aClafrEAm44O57TlM5wpj8S/FX9VkB6roURqJ0M1jPLyw12qGcptAhACNYeS2T74sOreLLNxLPk1oqzvyjjIITCo3F/XG4Vbvx0soW8Xk1ZgyXMk2SBIe9nyJGx9IOrjh2GmaAWI+UgDB9L1kSgwURBy9g2jGzAL/f8sm27p2MDXfGzd3CyPSvHVOgK9pUzFdJl38rqNYTy/jEAjgJhSjIqJwYE85GwKAbLikAYymNgxQBhPLDMuOE5IIy/4HQ8vjR7n8Z2H0Qo9cQDR4AwlYRfIy5WLTAKyxp1WBTo71BeCMIwxvUSEFmiDYFVCFAxnWHPUa14mgnTyrs2jGPvwkv0y3AcmvGdZ5asOXbfKgtb6T68Bt3YOS8ZsKgNevhoz9/o7/SPbsRSkprfi2IXqfkkN2PfMMUGcm0L/jSYMDZVEowBXOkDVHPA5+sevA4EEpAnMUOp6YjvhE4nQnZ0i8FPx5pGDZvni00HhKmbNQWmwYQx2S9jB8rnpF/ZeA57TvVDxlYycmTy0aYkHjNDzgL5XgjGbEw0w0wyCi9gx42dMI3AXERjn8Ax6msWBDxVkhEjwMJUgHP0G9/E0H98JvOd+rp1mDfS/v7s+ct3tu9vTn+Gv/xlTN52G+hUvlSjaSYIM/6jHx32783YjD2fjKXA6bDNPGkyYXgbtuB1D1wHAJgztRTHbX0H+usLSpkwlPGEPGrZGgwANOUPMGKj89JL8tsynpmzXM5y52CTNUxA0el1aB9eWXcz9qMAMR9WnFQiUlFYjcZyZGkJszxTew9qAb3y9lisGNB6YK9IMiQ89ml5LZtMWmSz/+SPp912xo6sHRYIs337driui7e//e2YPXs25s2bh7lz52L27Nl4xzvegbGxsaPUzRl7rtrmk+Jg3ed7mqWOsmvKkT1HmDDTtc5eWTx7KtT64UUAUo/MHlLBHlvKWbgp8KC36sKlcpGQkU0WNjUI46SYDrA9UFuBMA7o/n3gURMWlbUgUiAPCAFNJ5ZLQKgd6GRmuNssLKZZAuj0k/0kXnIDQAhBdyVuQziBA47Fe2/U2Qe2YydAXhPzCZz4eMz2ALc48xoAfD8+aTdqT2e3+/qw6uGHcNxtt8Jbtqxt++eK+Sck2VvPJxDGcW04BhureiRAGJPdlfIr794cFzDnBcFlZXdOCJmGvbPi97rlEPzOxu8kG4cyiJfa21qJ35cslN27tVNIrQihdOio7Qk5wjQIw5iWbs+AMI0YmJx94Cn5M4rFIH5vzjveEX9BKZWZiRRh/HtVX7BLFu67R3yUOh/KuJYjMxP4vVQguD+aBLbfFQfZU9n78LvhSjkyTmx0zm0BLC4On/FInAr+bGwCnHDsnp0M9EZ2lCxcLU29R2evBAD0kAZCOb+umhszflbOHUC4W0hKEFgg669HZYUI0H7nUnHzu/oNh9nvQVTZK18QtB5/AmxSOLqRK85zfkSx0L5If6WRUxMGLJYXsxEzW7aMxFT35IlSvRGyeQuOlKHyOcOhySR7RgeVOAPrE/f05bVxOJFox2wvUQuIt1p6TB60pjDpVkFk1lZRnQwV2tWyPKVMGIootVFhtgvLz25E0qyywBH/+sV7OFC5wKlrwouydSGBxBQI4zA7oUmd6KfCMuUYnnNQaMAHBbrVLKcmDOdEy2WZJuTIkteNWQ5YWLJhHdoCADh/A8Of/pSCT6OQ9Ot+/Dq87ba3YddE+6LWh2u+AXpPtCuoShkcY4caNcvn4JSiqQZa8kzVj4kIhyfXgFzgQNpAIJ7hyaoJwuTf0yiiGlGxGNXJKcdjMCGjAQBgXNeb0SBMz2JMVbIgzOgQRSRBmNCpw3MsgzEcW3IZkVnuBhPGpkBUwoRRwLmSIwOKpdqYnN85YfANECaXJcQo6h1JGc9qWCyHylvyWA5wWd/5OH75lfFniq3TEv+ed+jrOG7rD7Bkz+1wCzTWkWLhH26h+VDOk25Cjqz4eao2FVMhgCtZhR05oFNIQ4BIEMZuAa/6Iv754BCariyW63UjYO190qNhCz92nf6bl8wzR9uaIcWnfr4Rj+0Za9u2XZZ22qbWb9F/E9Z+XLCnIUHGOZ9Wv8L9ySL2FlgxE6YAhIkkE8Z8fiMpqUqtFjgsNEOKOR2qNqFoz2pDieOEcqnpaib7VH/4YRz46HVghhQqjyK9NrHJbeBQ0oMFWed63QYqXnYOGPrP/0y8Dvfvx87fegP+4wuGH1JwPVsbN+q/8+qc5tnYD36Ag9d9DHve/g7setOb2ra3/Hjv0Xzqqen9yIzN2PPU0kyQPGaIadQEeImd688q2zGxAwDwos1/gO7JU/GKJ99eCsIo0MXtvxvVpV/EWLNcQouT5L6BWw68xUty20a5IAzPnYNFvUglic3gOlkmDIAEyGJHNfipJEBSgMJoJowFVPY+ktvGNLVH4wSocrWv4IXXcmF1kex6LJmbCf6VWBkDcMaOrh0WCNPd3Y0LLrgAX/nKV/Ca17wGn/zkJ/GJT3wCr371q/HlL38ZF154IUbztL5n7NfW5h7Xh8/3NPGJ3gYiUp6tpOTIIlOO7FlmwkzX/E5HS11MDouATxGA1NsjiyhLuQJLboTdFHjQU3F1FjEHAXFt0MZEYU0Y2B4YiZkwlFio1+sgav7MBSdI7ivbKb7u3Ve9CG4UZxBZNCiUIwOADiN4cKhzN1p+tmCh0OE0ss45AB6AmHJkRhawY7BpAjsZfOVpsCllhwbjTe5tZ0wvuG91diZqwzwfzO5Kgi6khK30XDSvIx6vHT1HAoQprgkzXDMKg8ui0NXKmHwnJZ8H0Zem0aV3/VBmw6clUWRWfJQGYUyfRm4wrc6uuAi6FWkmDIgFZrmZwEhIOYZZp2yfCjrVYumMytiTImIsg+EKhHHmGXWu9IUxjqMASs5QqcQSPQBAhpLBfSFhIwOMRgDM4iwhXRiwTmB0u1EgPeXwXvMx+FA1YSxwRsATNWFS85IRwK82k+BuZEXYN7UPaVNyZFRKgVEAVPb5uDmxthUhBNxgrZy6fClWvPdl2Pftd6PWLQAc3xijcHw4mkkkjjfyVSGr1fLFnNTBGWxDh3hXOJbpH6dCygcALLR0YHf3xOZMWwA46aFb4powaMGRWa6+zI7dORzP1QqEsThF7eVXAABOb1GMDMRBFmrozEfDI+Cq6K5N0SQVzSqZfCRZmFL0neqMMdXvMiYMj7JMGGq5iexT3VYBmhqEEe/PmSjeJKgYmi2vCSsK2kLW9yCRXpcBwYTJBNNVf1JMGHVdwmYBEyZS14MlNnB5BZ9zmTCWgyjIl6QTn4vr/tfXM7zwMY5jHyt338dbMQv0UONQScunZ8kMxvJNXDMFfIclQUzOOZxUdmZUUjRbHTsC4Em2dZncma0zt+P7xAtAHlOyUTBhxL3vRTML3jEa10uSY9i3PdTd+D4onfGJUYrIEXNPZDcECCPBgDeNGaCNeZ2MjHMV/7cZENECeTxDjswM4mZk1NRP6e0gA4UVy5E1csYv4xlwtac5G9++WoIVs2cn27fks2MDi7sX4vRqzEhUS1IwLuZN+9ILsHT3bbA4g5cCKic7VA2vlKWK5lbPOiv3HJUFqiaMbenkjaIAeRAx9LTkedFASzt28BSD9vbbQThgQVwXi0wBp/8Wjg1DjFdEXbha54LSArtHw6wuAcZXT4/lc0e+/OVnthOGfeEX2/Dvt2/BKz5zT9u2ZgHpvPpqaWtu25H8fhuQJW9uLjPOOX7ymXX43scezmSUp238hiSrgnBeOE8yJljPtgRhmrZY0yNDjmy4Y59sK/0bEuIQ+tAwjqmYvelfUTUInNSTs/N3fw8jX/kKhv7rv+JjUKrXJkIoqCX9NcuBf/zxmb4rJgy1AGf1x3PPz7Rw7179tyWvYTQNdtj/vDj5vI1++zu57VobN+m/m48/3va4tXvXxC8KmKhFIBFrPrt12WZsxg7X6infKK9GimksBTSk67LkWW9TgMLVqFskJhSYAmEq838Cp3MbfjlYzkTjSCUulbDHKWO6tpUyh/Pc82UmCEMieK6fYcIoe2DRNzH34MOYc+hR+MY+XHy3qCaMwYQ5uCm/jWH1lvRLEeebEI7Ca+lD1pU1mDDjpD3jJt2/GXvm7bBAmA9/+MPwPA9bt27FF77wBbzzne/Eu971Lnzxi1/Eli1b4LouPvzhDx+tvs7Yc9BOX9yHSYtD1Wz2ShgWjmbCMKOuyvMDhCGEoLNP1nOQWZyun58B3NkpHGkNwsjgqJcCD3qrMQgDQsBDiqg5aTBhUgGl7vkJOTKHM0xNTWkQxqwRclyfqItShMyXMWF6XvoyLbkg+h+WypF1eg6+3jOFXX1P4fbj/i+3zbwe34Rg8HC1IgrpyinIHZgFd0mc0eBUY0kxZmiTVBuHgDYgzKknxX//5Iznn/b1b4r51Xi8HhkmjK1rHqX3TOZwHxsXASHbzndo5jpi7OUpCe2Z3JN4zY0sQNPyQBgehRqEiawQkRXqLENq+zlyZAytPQN44sQ3wcGCxGd8PAYfru+cC9SHddazCtCZ8oREBwziC2M1BYuDcIqKJwI1wwMnAwDcetIpE0wYGWBMqyQatXL28HngxMIYlwF1pBzZJeeiIj/jxMbwhk7AYP8d4AeT7eWP+Yxh2YEsCNOdk3mp5MimmsJBDg3QaMVAT6JtX9V8TQDbx56tP8epg5cBSEmUEKLPhstj1u6RzCFPAgf2AFzjAvWrQF0imBppUMUiTYNRkj+vzTmwS2+ErEaoaxJUJAhjAoyhBAIIi2D3i0Dozc4VWHzGSFy8fjCmqUeHDukxeZa/EA3Sqftz6KvfzPSFB3EtE9XO4nZxZjCNNOCkjFku7N6cGkSmtB8nOKl2ASa7xJqggrgZ6xGgmhqD6ToricMzDouEcAzd/S7OMF7E4lCAlgzwqrozUSs/k51GqiYMBeFGweecTZ9gwqRqwhAbUVAc1IlSMgaVifLN8MGpOOvZOgqBXxOEKaunAQD1FGARlbSPGIeb8lvK2jebCtQVzAagPPhat8ScwQk1QJiC4GgQjw0Bwshi9dzPBO1NOTI1BXi2h5oXgzCV5ggIC8EoMNW1UJybkwRhlh77Ge1PJ4/P9XlymQDkMCAoYPtGjAEahInbNAtYSFq8jDDM7utAINeEoV05DBfGMrXMXvnUX2DvLHEMWwb+lVEFwjjAwv5+nFIRQZqfnkP07xJVyFf6hPevInCt2EcYqAxgslIwkFMgrT2rpJAUYiaM5xDN6CoKkA/X6/rZs1iomTCVMJkEs+dtb0ffSKAZTrYMpFMQDHUKv2Gq65mXsNVBKqfcb36m7PG9Y9Nua0rlTIcUE5A0k6QdCHN4wScaMex6cgQHd0xg/GA7+bJkh6/d+stCoI8ysY6q/VDLEceOa8Jw/QyrWlKMRJoJkzme8dONtWvjF/5A7u+3NsSME5hrEwlBjcS/1uZsogg3ik67s0/IPb5pljE3VOWyXo/aS8GliWeD//AP2b4EAer339f2WEU2+o2szwOgEJyJBtPSxjM2Y8+80ZBh4337URsvTuJR1kiBKG0lwFJARvr7pr1o0aV6n6hs28i24r6kfrse5ssoKuOpkDWzbPAieWAGHOpM+i4OZ7nnG1IWAyiEw3MrWSaMXIC296/BKU99CQ5twDvm8oJ+FtSEsQA/tZDl7p1YzHpW21eLo5BFy+W6Ipgwot82dzFpyNmWGeczIMyzZYcFwvzwhz/EJz7xCcwzM2ylzZ8/Hx//+Mdx/fXXH7HOzdhz3+b3JjdiZUyY+riYQHY+MRwzYZ4ncmRANls/XVxct1MgDPNBONFAi5/K/q16Nhwq3lOLS2v3vmIQpjoLzIrlyFwaoVGv6cw6s0ZIu2JopdedkAQIY7Mgkf2eOZZFUO/yceOJX8Box/7cNifM70F6UyLYkpL1U/FBbBvVM84Qr0//LfRWxjDhD2O4I86c6pra01aObNmrrgAm340nu/4iro/xa2rLv/VNzHrLm7Hq4YfaN36OmWXMFdXuXz1AYMqRHdyRlIAxg4Szx6XDki48IE1tYKs5vJofbU1lNxogzLEGiDJkxpgVCBOGsRwZiXBqq6klyaI8EIZxbFz12zg47xz0Oe/FgYUxuthv7LK7W7OAA08AUvaQSWCbGJJPlQYzuwIAmDMsaNGEM/gyq1kVvU9bZMiRWemAlwHCDNYvQ8Bs7IV0YEn2Git6NbdscGoBURzYryOl4e33AMsvwdubBE6KhRRZEa5YckXm+PtqAqB6ywLhp5iutGsnx9mJC2LNOQKgZdn4xGQsC7bs5GTQYqOfP05HPHHvAkLgGvM2mczOh5wyzQ6xSFMzShzmgbLs9To0Z5EOeJIJqpkw86sExB3Brbtv0pmkLRk0JpzC6+oDAHy98tvoAUVFglLWl26P+9JqgknQyvVsjDvdGmxwTjw52/fAqFnG4joTRbIvPKKZTDpqe1npTBiApk2weHwllh34HTx4zt/pa5ZnpCoCodMCYSgTTCID7BJyZEUgTJIJo2tkFGS/xpnRDBZnWoovLxAo5oKsHFnQKg5GRZyip2Zc5zYBxMAAYcZrRz5YFBj3JGjTl0YreY3LQBXKOJzUcKIlmXqtVgzCqJowZcdn0tfiMECYgvamPJzFIp0d6SAno5LzLBPG8rUUFSBYYB0NIRU02S2kTyO7gYpjoyIZxXsrJ0D5SokkGhZLVKhC3DYFgmZ+bSDGoOdsZlEwCfinWUm6+1BylhSzezsw1rcytx0gggpRiuEGAHXJsA527AA15KmpfGZCB6hWugBO0cUYvnWZFQc/5HMWBYoRB7iGnMjnXvQ5fPtS9XynghzpwEa9PJgT5jBhioJRI/W6rvPihPUYhIm6MJJaLlu1cVAJwniWuO8nBAFGq6LAeL06NxOgOdqmguTpGoKHK/X1bJg5x9BpSM6FqVpQzak2Momp577dNQka7bPAjYMlXl677e7CrHPKuZatBmIQJjJZq3Lvpxho450U4EkQRrGgubFf2/Unb9V/N4+7IPf3o5FY3pZHUSyrSCi4FYMw2VPkCTkyJycpJmNGtrhUcUUzas8oGexrf+gD112H1uYtifcaj2UZvYdrzEgC6b7qKv03p4cxHmZsxg7D2jHtTFvzP/fi1i+vxw8+9mDbtmngo10CTfq5L0riAIBT+1fimOHTEu89smZrYfv0mtsOE08z3TlxCgFSylgmY9BjLPd8ubGf9jwP6+0wX44M0PE2TgCrkkwmKyLCmEyY7GfZ62nW/1PJKRZHoRyZUv22uOGjMheNgtpg/qpVor1KhnsW5Ul/0+2wQJj9+/fj5JOzG3Nlp5xyCgZnMgN+o6ynkpyg/RIQZnRQOJYb7x/UBYSfL3JkAGC7yXMrAmG6OuOgk0t97Vx7TjLoctriPkMlSFyHsS/+1JAjS6ecuwmH+JJ96zBVr4EwVeclvhftdKftsto98+bqjSYAuJ3VwrbKeivxOds8G8j13ezvicLZKrte/isLuMKu4nc+9Sp864x/wrAB7Czb9fO2IAxxHHztpS5+eerzZ2w9XauecQbmvfvdz6t6MMqG98Rj7EjIkVWNmktTqYwgtfn1zBhmlP+bjXGRuTwt2RCVvW8nj/2tS+Pxrjf2UaQD3tSKcHwYIpKSZNT2cShVwLRv9Ek0KjEI8OTKt6PWIYCFZmcs99LXmAts+lkWhDE24ku3ZeeDxoicNzhFRQbUjt12Q/5pUo6uhgz87U9SsO2UY1ijFmwts5gDwqwUm1jlUPOgBSa91yXVY5KNCQHe+GPMuuqfNPigjFohHKt8LgCSTBgnJW8zq8Mo/AOCGyY368AlALiVZHuuB0VybhmUQZMn3BrmVeJzoPq382vCWKSpg/uzJl1MGfOusrHuWaByI1TprGkwygNF13Efx9e2fFTXKmq2pFQmo6j2iHNjbjcGOkN4oWQ+bYpZVKzR1Jss13MQ8CqmKrJ+Qw44QcfH40CzAfTTguKeQo4sy4TJA1UUCMMthvmT8TXkgJb9S5vaqKoxyK1iVk7EOCok0HXIAJEtVsSEITQlRyalAIIgf21VPg2VwL9iw+RmW4dxHSQlMQpi4cE9xczNKGrhNffEx3ILatno9oZ0wZ/f/XelbZ+OJZkw5cGo9Ma9TAYopAzpp5qVtG8FqmYB0SBMWXs1PDgooADhgv5zQ7KRgMdZhoRnN/OM6ZpESkrW87sTRbWp7cNrjSW+FtkN9FRdvX6ZQZJZdux7cWOt4bYCQgGy7dbcvgsmjOwHIjAJiLcK6t/ETBiOSsVNPN8ZY1w/1978mOkzYfi4Bz/9af13+MQTuu9VvxM48eXwOEfLi+dR9VekZAkJEkyYEwdOxBvO/u38vqeCCLRenlnfkvOPa1uoKCZMpBIVkmNnbLIBIhkWDm3AC8QcXQ27MJZyu+bZ/QglCGNZYr1yAUxWRsAQgdkeWn45S+dIGudcB1hIav9Ru+feZ6wfT9fMOWY6IAy74/bE62a9DQiTAkXa1ZAJDCnKKCiPGOZJaBbWhGEctlFbrSn9CRNUUkwYrhN5xLHywEPzLNhEDNJ6Xv5ezlRR4JRqidL1FRuMxHJkOR3Xf1ILcP1pgDBGosmZW0VPP3NnQT08w55c1n4/l8dk2fH63yruSk5CRV69pMlb4jmWVGPwORoazrSdsRn7Va1ZC/H196/BjZ+bHoC4Zc1uAMDESPt6Y+mEmbZMmFQS1Z7h4rWV0hAuTSZnVKLi2EQjYHC64me/LQiDrIRvke9GOYDUfu/sg5tzzzcy9iOV/sUIbCezryU6MUZYHltZf5qpWRczYZ7wk9czD8hlJggzDTkyBcIQkwnDnEIQRslBMxnrGP7v/8ltN2NH3w4LhJk9ezZ27NhR+Pn27dsxMJBPd52xX09b0Jd06sqYMItPEJuPao/3vJMjA7ISXkUgTKVi66xDl/qwpIav6yUXhN6qC6bkk4ygnmbCpDLXQENwgxr+OxtvhY8wliMz2s+tzpV/ieP2zE32vYwJUz3jjAQTxvXaTxM9hrQUJ9lN/qHJlg5gap1yAqgpiMhrS6SEEms0YVlEMFkIx/+c+zcYGP0H9EzuagvCAMC/X/Hv6Ha78dFLPtq27Yw9+1btOjJyZE9K8euh/fH4ZYzr4oOXNmKgcla/cFx5ymHyfrIOALB4f/u5idA4C9DlHAtcoUVfqxJ486XUlQr6RVHMhLFCOJyDWTEIw6aSAfiV+38IiyU3iVOdi7DxmMtRG47rF7nUBxwPRDqoNFc6MCtHFqjniDM4loOJVx0HvzkKAGimHvmIMVhSD8LiSaeRpdJ/GhEwR0oxpjPaAaDj3DeK78lrwaOWnvP6KnOyXyAEvX4fDvZSWAbrhlu0lKGnzARh7FRmVGAEYQgnqI9swWvCmF1z9kuWJdqf5gvwKz1mrlwXn+jJPZfqv/P2FKzR1AFMh7R0cH/JkJurjW5FLa1t3DtvSrM+7GhMt7lrz10AgLoMuFg8gt8t1ts9o3XYHtfF61WmNiCZMDK44nsumrwKSoTj3qplN3RDn/1snCRgAP15klsAwGmkASR9/paH+iOPZNuGKtjDcc6ea4z2bmFNGPW2mbVWlEFIGUelmixQbzMHU62CoLRaV+V4V+cbFQSxVV8iBcKoou95TBjGNAip5gAAeHTfo7nHFv0P8JKH43Nzm+U71nAa2cW/ipkgRDsmTDNVR6dMBkgwYZLPV1kxWMWE4RbgyGSPsvY1osYtBaQ0IikAbbiS9+NK7kFucMEzm3khRyZ9NznPVDpmJ/Ba6lQyjD5qtdDlO7q2Xq0VoV/CUJYxt3LJEBEgjJQjowAL8pkwEWUacOwA17KuRfVyuA5wMMzt68RxBiCfuV+cgcqM/N6zB7U/6fC++PcPCGlJE9SILMDzugCvEw5X8mryAnEOTmMmDLMALzV32DpYnBwf4zckkwd27nwsl1WoLJT323UsVCT41Qop7t13Ly7/zuW4Y9cduu2EwaqxaaDrJVaijgyn5ZjKYlAJnFXnSXndM38Pbxwfw6QvrkejugDPlJn1N4jjoHrO2fFn+/bmfeWom2Wsne2AFcVYWh5acEsysJWx1DhlBckB+vdTnxclEygLGiYIc/gsiCLJO8pNECZCKOtgmvUXTElBAKAWBY96EsdUNWF4KvFBmedk2WviYAabxmDCMBKVM2GM7zELcGwX5LSTMu2S34n7+2c3ir//74EtbdcQEILbT0s+cVteeCWa69eXf6/EWCN+ttUefPh/vpRp13zqKaMb8Zx84LrrnvZvz9iMFdnmBw9gcqSJ7euGpveFwyisHqR8o/ZMmOTefOP+fH8DACgL4aVAmJXziiU4GyGF2x/XZArb1OjKSPhaTiEbjTKWAWFesvMB1HLmbXPetx0Lju1huLMA0CrpYpH8v8mEOZCO6+UweUwmDBsR8+7ARDETRm00LR7BlntIu4QJczjjZcaOrh0WCHPNNdfgfe97X24mYKvVwgc+8AFcc801Od+csV9X60rVRfFKaoesPE9kcc9d2h0zYZ5HcmTpvrqV/HO1LEtr+w7UF+mscC/FBumpOGggp0CxdPIyANXcE7VDrAJbPgkNObLYSf7nS/4ZNuf6M9tNHqvdda8Y9TK6nPZ6vb3V+LftnHO65Pg5YKnV655Kh+ZvWlLGwqqIBZw1xG/+yWl/AgAInRY8JgLEZBra1uctOA93//bdeNkxL2vbdsaefUuzzJ6O+Y6FPskKW/+LvTr4M9mMwLnIJDkziOcr15XBz5RT1ZdSxEpbIlBuSDHsd2xc0Pv78Wd1mSWn5cgiHfCmVgSPc1ADhEkbYxyhm6Q7P3nyW7B36evAxl8Y97c5B6zSr5kwvAQI50Zwk8koM7UYCCGwPR+WzAy3eDo4EQcY0zIbjY6kPGmtHmng2c4BSTp7xDkx2xOXPmjGtRQKnm3P9rBlIQEzrhMvkJNL26OV4gKFFx0XJ42cdOAiVPc8jOpgfFwvxYRZ5PWJvpeAP1XPwesmBOuE6rSp+Jod/MQn9DhwSKCZMBZ3sXtyd+Z4Fg3i9lYLtgziDvDYD1NATn37dgCiJozXI/p62aq5so1wyE0ZIRbEx/Z8DwGqCC0BBjamcvy8LVsNJowBfBQFr8IsE4ZaLsa/971MU95syL6nxp5dyaXsA0bmMm/fl4gyYFZSrsFmXmFgjMibp+XIJDOAhvnHV5IGkS3bq7pIeZtcI9uYGyDM3pHs/df9T9X+8Ovl4z+ahs7+r2K01cJJw9thM9p2I9+cSvalDISJcuTIyjLUdaDSJnBlQL0smNqAKnpKYVMRULALSEVMAm6qLpACYTyeI2vBuA5eKkaOJ+s36CaWm2H0MbsFz7E0q3yyGcbpj8a8wUPJUDOYMA5TjOKsRaZkJbE0E6ZRUHdIrQ2cMCye3YXOWhykP7RrEkN7Yn1xTqmu9eT6NjqlLHFPa5l5QPGPEejcuoDAlbVmInmOgROzBW9unosoNBIbUtn3RczHYPuOxOtqk2E8GM9tCyTlyBRLuxUxvPWWt2KkOYK/uOMvdNupmrhehFFYnGoQ3KF+hi1LanXNhFlelfPeqa/HX42OYco7JH7HYLcebWttMmp4WBa6r3xR3Fe7fTLT0TAzUW/TgXLN+pAyLI4svK7m45pd02DCpAH/NqBKKwXAt2sfGEBQOyYMcuasMiaMG4nxYpMIkdx/hVKOjIPr95QREgGwMvJCQDZJRP/O1FwML8/6hyqQqeTFFKPv3GZNr09qHU8EPQ3WiJIP9F50We5vK5u8LcvcI1Yo5r029rUXJvse7tuHvX/9/8Tvp5KYpmNTd/4CAHBwzpm44/LPYPOxr8bEzTdn2lVPiyWWKqecov8Odu487N+csRlrZ+YWqx07D8jKdJVZGuxstWXCpOpslfhu9+y9Bx5NJmazoMQXC0Vl4LhvpV0BR2p+J7ZOlEkbZVz72MrmNSdy5YdNuVvHseHaHjjyj6vqK+YxYYqSyqncKzECXFVL+sK5TJgc+bK33syK5cgiuVeZJhNmpgbMc8cOK/L1oQ99CBs3bsTxxx+Pj3/84/jRj36EH/3oR7juuutw/PHHY/369fjgBz94lLo6Y88HK2PCODLTLwpoXBPm+cSEMftK4qzLMls2ejIsGRx13ORC6dhWHBAzMx5VkCsNlFg2QktpfMvs5QQTJl5wlvUsw38dGofKGHQMEMaySNsM8iqNsx3SmRB51lNxUd/9h2Ct2TjDe1fmc5Xlado91ThAYUmnX2UmHbzuYwCASxeLrPJFdgdQkdJBJUCfaVahQOeMPRfs9e89FwCwaNWRkecghCA0hrWqQTUhN3fZmr451OGRbbhvlXj97UuS4+ctP6OwKU/UW1oUCWmnyLbg1Fah0xLUa49x47ByIx0ZIAyJ0ME4uCFHVhtIghk0Ygg8waZ5eMEPC8+7I+jBw5tu0EwYnvd88MwfYJLFoyTDHK8KooKNPMkoCCOqgRKVFd5xuXg2uyd2JH5q09ghXZyR59SE6e/skZ/Zgk7emornvAKWm2u5uOmc5P2YLgizLaf+iLI5fTEgcfGO16LC40AqmfVEpr1aAvYtTl7jr8rgwHlYjk7fRoOJccByAqS81dK/4ZJQAygEHj7/2Ocz7e0o1HVVHBLE9U+4h+Ujp2DR2EoskdnXsRxZBK9TBDt7Kg6+Ty/WTJjIMUGYMAHC2MRHzRPBjKkdB7O1Foxsf4vTmO1RsDHjlGbWD2bn349D//4fAIDZqbhc5FQKs91iaan2IEzIOByW7IvN3WI5Bq5AGJlZJp97i+aPUS7BmZbUJVTazHnXhvOYCcOtUNNuzuw/I78vAKKUxOjBWQUNpYVBcgPWrk7c4dofPvQ9fPKXn8WbnvxpWxAmOJiUMCwDSSLKs3JkJe2DlvhtnwZwaiLwzkvax6xjBsLEhtguGgIFTJi5ZDwbUOVMz5GKxey6WfmfNAjj2JPwHQtdEvAtYmY1128AACweBriciGwKNAp8aDNYstxytSxaM8jPMoiLcTMQxxF1jeT5fv/jD+Pb//Qg9m0ZE20p14wPt2pjakzMSeftfxP2LrwYAMBqsjD9pJhPKAGaHuB6Ym4ckevUcI9MOgJHhbVAy0AYCRxkAiCpecpmwFRQHJRVgSjPsRIMpDyrNdScGqDvuJqefzuCrF9sT9QRSRDGH3pYvOlWQQCsokJWt1mZnfne0TJn3lz9t9XZCd4yADj72fGPTfbLk/uKs6oBAcIcF0rpvWnU0klL55Qx4gDg0HgSkEwzadJmMmHGh8rrDiEn0FXIhGFxTRiLhDo5R9WEIcgyYZRMmFq/vvfy70H7mjnT394FF2HTnWfhvpPekPms69JLZEekbI7MIL+3wwWzVPJGrFKgT9FYl6klQNJ2ErHDn8vxcaxGriyof9KJAIBPvFqMgVo1x5+SkmKH/vXTpb+bZ/vf8x4AwKbjXgcA2L3kysQ91ib94s6LLkL/b8fX7/kTvZix56uF02Dc5UoFFlgQMRB3BP7cH4M4Y219t8lZyf15UNKftVM74UdJn4eHxU9JK6QgdjyPBm2YMNk6ijZQJEfGeIYJw4md61+ZIIzlOLi29yStOmPasgNm/6Zfs2eyMS77C/zuRHKD006OzLQiH15ts0VNGFVj1EUjLFijpt/1GTvKdlhe2OLFi7FmzRqcdNJJeM973oNrr70W1157Ld73vvfhpJNOwj333IMlS5Ycrb7O2HPUXnLKfP13GQjjys1O2KLPSzkys46K69ulQEYoQY9Gdx22kiPLuTbcFUBEnhxZHkCldIBVxlcFAVTteSul+Wyf+Xv6b3Ptspz219wejusGRNMAYXqrLujUCaht+3+Y42YLuvqOBV/vDFRwyyhgLfs0dXus6Tz4kX9Gh8wkrTu+XjisaYIwM/bctjlLu/G2z12Ba9915hE75oalcYB3/1bh+IzVAvzBpI93TMTO4WV/ekpathUAsG1kIxz5PB3Pk1nMVz/CcfljHE1DTubMSOjZdlQ/hVc+9RfovEcWaLcIDqlxqpkwQUKOrIszcKIkoiqo1ZN1bMIaEeAsZ3hs4R2Y25sfsLCZi/DQekDVYTHmgc0vfBUAYKIvW9BY6dQSJZ04GmomjA1gw/B23ZbREIEEne3VYsM+521vBwD0jScLoQ5O1WFFoi11sxe52hGDANT2gWZNg9BOAWCyd2ovIhvomYj7FHjtMx9rp7wGx8hsTTdHTmPVwphptKdnk/gjEiCRPdCRaa8GTeBZOOb++/TbXF7f09xV6Km4+HEkrpFiwphgRscFF+gMLZ+EmglD4Grn2zSbhgYTJoAjHXGn1YtrNv4xXr7+7ZjtiPNoTo6J37Ui+BXRf9+x8ZXoas2gmTI1zVuB1pj3Kx1wiAvCpuQ5doGOjiZP33V1sPbWM7hmCLSKin1HUZxJR8T4zpM1AYDG2rXx3058b6MSJowCCjnizUlR4I0yDpemQBjmohWx/DoySn6PJeXIFMsrbVxuIFuuYs7IGh55m1Ya1w8hFtVATxU5Y05aFCbnhzrjpYWk00yYiVZ5wBMQ4/T+/fdjtDnatu2Lt4vx/5qtd7WVkglTwcey4GjEWEaOrAxUieQm/OQda4Ebf9T2+GrbI2rCqHtadPAkCKMA0wbvyAQvEnJkEoRZ0X9c5pBOWiaONEVtEoORkWe1e0UNDy8SbEcOATb8oDtbgw9IgjCOY+udeq1VxJBSSQlMJ7oQnhxz139CyAgyxvX49Ssulp4cI4IbV4q6LeFBUYyeTYpxV/cBEALHYOIB+jEDOMc5ExvBmzX9/q6h5O/bRcGm1LzpUKBewgQLdU0YohlI441Q10YzbfM+4UfYtAXbYxqM7Wx5GSbMY5vvjkGY+g7xpuzzMRAFiie7lxf260gbseQc1tMDYtsY/c53Mp8902aC3u3qEQQRh8dNn6VdkO7wmDBRal4qq1UFAKEhq/iLb2wsbZu2Xd1zC5kwlHMtHWgjAJOSiaomTGeTw08pkDA7CcKsmrUKi10xF5jJH0SqC2xfLlQBqvUXZH7fXbgQAIy6bEoqk6LaFPv73YsvBwDUfnmX/l6wK2ZuerQTm9YcBMlA6O3NslpYvz/LiiKyH6EN+GEHfmv3yZms/3Cf2Ku2Nm067N9VFvixH9hEdg5gTTEPkWo1U1tpxmbsSNv+sTh43o5x19ywIcP4KLOQMnQs/hI6ex9CdcmX287BlckkGPHIznLf0MuAMMXrTEg57Go8h7QpLWhIpsrXxNVzVtoixsFJcj3Y2rso93z1OsEZHN/H8Z2L41qNhp21NVaXyWXC6MTf1IcsZrZ4nGPwVGMuz5Ujk8zE1PtFIExNrg2ExUxdl3nFPpDsz6w3/kH+5zP2jNlhe2ErVqzATTfdhKGhIdx333247777cOjQIdx888047rjshmPGfv3tspVzYBFgfk8Fx84pLsLlyNoiUchiObLnEQjjGXVPvIJ6MMpac0Swxw19WDI4msecOaCKLxAThBHth2oBdo/UE/+por87l12DX1z8CfQPvxa7j/lP3H75Z3Hv3afis396u/5vzfeu0rJophyZXQKUKXONyXt5x4G27XsMOTI/5zwX9lVhp5YUUz9TAyvGdRj92tfQ6YrxVI/qWqaGWDMgzK+LTaemx+GY0+XicU84NaODIpiz8bY9mEeTY7J7Sf481aIhHOmjnRHtz3w+a5KjFcWBIZtHmOpcACIzbshQ7OCNqkxTDcLEwfTIitBJXDBHbDxbXg8m+0/C9/7tURyUhQ9ZU+rWhzVcffxqvPhNp2Ng6PFMn/yoA/84u19vfrkBZPBOcZ69Y8rRM+RtdJBOBhgdX2fvExD83k2v122jKIRyFxyZGVU9UWQp2inHsGfbYzqomRevtiyig5/U9hFufSoOXrr5gO+FCy7EeAcw70AsJ+Xy/LaXLLpE//3R+QvQWPliAMAFCy7ItB3o9LC05/8AAIsnVuLzfT1wx4WkjpMzRPR45YDVEQfMOxpyk+52oMsXBe4BYHsOqGR3d8dMRivQmfEOn4Wqk82ctw0WklOtxEyYKA4WhE0JDu0SNUUIj1CRIEzFtdCAjx1zRPB3qDf+DVV/AQD8aiccy0Mk5chCt0vLdSjreclL9L0KbKbBg4l6AcsiiuXIiC0z4qcB6pu60rSECaNicoxQnXlcyIShTDNhFMhjcweE5we+iYwOq3O0NBMmH0RSQEFTMmFszRLK9oczqrONQbj+DV6QKQ0ANCVj0FMHHhsqLtwaphgXn3nwXwrbKvve5u/hz376Frzuh68pbRdldMXLd89hWvanJNiZx4QpC74qHXGCeDyCFdcG4nrbQ/VG2yo6fKQYMBScED0/RtzLbuYZ18+pKxl9rpudRByavC/cbsBzLPgSuGkYoJ2JsbmLRKD0oeMIprpehrtf8DH4tFjaytRXtxxHsxLDnMLTQHxduMW1VJXF8p9r8xBe1cfVf3xK4vN1p/4p3EvEvKsYMQ1fyFyqui5/MyzlZRXjDBx7fjkAa2QbAJFZP1pjievhFtSECQ8kfVSboVgPHUDLkCPrlgykVsTg2TlzE1UgbIjGkKfnAWLNAnV6El7tyTu5BmE8SzKO5LzUbYtg0zPJhFH1N9RaxaYMFtSz5EebbJC2IAxl8IwL3GrmB9yUZViX7WrCpADydsyZYBp1aZSlAfItvYuLmTCU6X2iTSIwyXqhrfgYV65NApLqeTaP6RIFMBtShC9/OQAgdOM19f8u78K25S9F0xe+nK5BECkmjKoJQ+FGos1kz3IAQDQSsxonf/Yz/ffrHnsP7vjaBjy4fQk2rHwDthzzytxzzTPbamIkx4dQ676FTrzpoY+if8+f4MmT3pR7jGhomvUz2lhrJAsGKalUJZetzFux4oj85oxNz+rt9KqewzbeCNvWwFJ249o4CTYsSHBSFh06lAAmmxs2lLYPIoYrd1+Ntzz4cbxs+ytKmTCs1cr4631+uRy8L+XILCL8VdIqbhum5tuozXydAdktG5zmj4k8ObLxSnfu+SpmNuEUtuPC8bvBMnJkqo16nY1dFIYzFAhjETgc2HdBPptQv5fDhHnkGIKD9YO5h1clDyweJeRSC30guTa5y8Re1+rKT+SZsaNvTzsVpr+/H+eddx7OO+88zJrVRhdhxn6t7Q3nLcUTH7oad//tFeiuFE/Q1R4xmU8ON7VEB3ke1YSZs7Rb/+1WyjMP1OdO5Gnn2sspcH/KclGkk1omCCPa/cV31uKSj9+R+C8wKO40J2BXZJWueJGebh2ey+76S1y05n3odQ+vJoyXI3Nw8sIeMK5kJ2Q/EkwY+ZmXXPAVE6ZFW3pDSdKFzWZsxqT5joUhGVFbd9tuPPKznThwX9JxGbQZdg6bYzr5PNjqEctZHS0OjLXG4m9SjgfOfX/82jdlBeW/ajM+thehlBdjJEIHccA7ZB0JpxM7V/4uDqwfxTv+6ZdYs3UYrCWBiaiGlx1/LbpXLMTpT3weF675QKJPHqtg0OoEVJaQEfhfct9tyRMwtYapYhGIE3aqnTrjG4gz+wGARlFckF0CJUT+jpXSqWXhHN0XixQUMUcswzb5vf/Sc55bIEfWV+nDSA/BuqVxAGBwIL8g65+f+ef67xu2/gitVS8BAFRyitJaFsEt896oXx8gXag0jhHnGfVm2sdOdnLM7JWqL7bTAd+1wZmbbJa47vG1rCBEpSnOyeZVLb+Y6KPhiTv9c2DLTHpTWisaF+M5knKOBBF8KX/pOzYa8DHRoQC2eHxERlDL7+yGQzxMVGIQRmWx67O2bX2vIptplsh4rSCwa8iRWbYIbhTJkSljxNHSLOJ3qiU1YeR3LFGvAQCigsBeRDkceV+absy0cUHRyguOFTBhemrzs20BMCngHMjTcyRrguYd22DChBbVDDReUG8GAFiU3M12N3ipxFiUYlzcd+ChwrYAcKh+CJ++9UP42icp/ujLg6Vt06BLWzmyVPC0DFQxa8IQmdBR2l75kjwejxaAZgEwNG9E3KfIpppBVVRQVW2QCacg/b16riPczp6zIUemwAJHgjCBIblRaQwnvjbCO+DaBL70gR7aOYoMxQLxfLttvo/hyiUI3S70s9fm9huQhWmlObar5YuiomdJy5Fx7WORHBAmaEQIJcWP8wCu62uWu7LhgVOx9r5xhPv2gbXEuA0cJGr9OKf/tjoz2QHx4aEJuTZYALiN8//5VozXpcRGQcZv/b77Eq8dBjRaxfVGQnnvXMdC1ei7l1ObrSHljiwaIGrYOsgBADtWfRR3X/RRNHyxBz5jO0ckfVa/U643HeIzR7JeOXkGM+nlBKlYL2bWMHmW5Mgah8GECSlLMGEevWVXafu0XE1Z/QIgm2VeVqsKyM5j5Z1JSeRxVsKEYbCZ6LtNQliqDouUWiQAJruWJr7DrSQTRnxXPpdmN6pV2T4e2wvwMexYvhr3XvhPyb5K5p8ao2c0p9DsMOoKATjwj/8U/96AGNujvcehIxS+7f5hH/sWXoJdS1+M4Vkn5Z5v2mzSwgd++ARYvZ7MbJd/9wYxyHtozpmop4DM9SeciGD7dhy2SVaLZ/j0kVPJ1JfRTBhZX9DqFrGA6plHjsU/Y+X2v/dsx0l//zP8/Mly3+TpWmMqwG1feQr7No8d8WM/umsU5/zTLfi9/75/el8w5qEn7t1X0lD4Bea8F+7ZU9o+oAyrhs4DACwbOxmNEmCr+dhjmTk1LEm4eUFloWbCuK7Yd5NmcbwpyIAw5bEpNS+pPSsnTjF9JggS+wgAsMBLmTCEM9iuB8vvBOFZ32f3bGPndzihS6PGiw3ABdCSXeM5/TeZMLNWiblozxxgKsxXf1BqO0kmjI/33v1efPKhT2a/IP0Cu1vGI2q1Ulb9jB09mymaMGNHxDo8B04bp75/ficsmyBsUUyNioni+cSE8TuMot5tmDB+p8x0Djp0TRjXzX7ntGMECMNJFoRxHQtV1078d7CnOMvBr4aYvSSLaN+77Ifonm2AMNO85jaLUGmNTStjrscApfKYML5rx6CLCrAYmytLbfpTIMzwb/2h3pwHUt9yhgkzY0V2cKKFugRhWvUIa67fmmlDIPTf81g4hFi6PgC3hIxH2v7p/ngTylI527zFMHcyuVlW470VxOM2tFvo4gCRxXvN4uU9lOCRXaOY4GKj6UQ1VNwKLF+0qbZGYKcyXPob8wDVFyd+hoYvuCKvK7LzcoMvi0o5lS6d6Q0kQVJKIyhgxU49o3YKhAmj2WgHwoQSP6a2j+GnuuPgZQHAasvPv3rZBgz03IR7l12Pqd4sUwlAhk3SlMHoPKkZANhfjX/zzH1x4eKeVSKAumu4jod3juDhnSNIh2iOu/02LPvIn2DfbFmjwe2C71iwK8UbRRrFjKgqCePALhx8d9N3M+376nEw3Wnsi51sGo+ZaLuoX6Piq4RHep73XQt17oORdCYXEDXjjUalqxueVcHGhVKOzO1CdOhQoi+cGrWBCNWgXb2gqC6PIoRSctOxBaBTVGOs+6qrRJ9SYFnkVHRmWNrCQyKgbUdM1zOqHxjObRsZNWFadkNv5Fy7kR+wV0kDPMKxt/wcfiuWYcgFVrgK7idrwkQFmyx1HS2Lg0A+J2VMmJQcTWd+fXVtYQqEKSrqqexTD38KF27gsDhw+o7yDdn497+feN1OjixqJX+7TF6MMg5H7nAtlbhScnjFyiGcxbJhvKT+ggR6O5pM1xKyYOVuQlVA0OIUhPO46GlkY6yePqdYjsx2FRgjgJOWHd+LgZFkrSlCCDzHwuwu8Tx35tTPA6ADpQ6J1xefnJLfFkmpJcer6sz52WwkNyNXM2EItByZG2SzyydHmjpYwtGA64h+R27yuWOWg11vejN4S4zbyIaGf2nI0Fh3Kl792F+jGoqAJlGZprJvzAI4dzDRjLDlkABUnDYArmn//sCnCj8LDSaMZ1uw5VzpGYHqz679LABgpLkDgJQjq1D4wXjyWF4PthwXM8c0E+Z3/ke8MeuYaff5iJtaEJT0mElhetaYMPF8mFdUPt2209gnbF9XznZIBwxbbeopsIxMYvm8FzSmz4RJ14SxOCuck0IW6YChjRBEgTBGPQU3JS3D5PMcMa7Hs5LD8Wvx79SDOqjlFBZjXnP+B9GUtWdUTU4mx8YraxNo9gs27KyRpzLf9Y8/HgDw6JnZOqAAsO60t4MWyI+attLZhs6ggY1nn4Ptr4sZ2AoE7wtOTLS/74IPYbj/hLbHbWfO3DngAKJqn34vsisY+/Z3Eu2YZsLIujiTYj4a/frXf+U+zNj07EM/FuPvz7/56LTa7x1rYHyk0RZYVXb/j7Zjw5pBXP/JR552H4vstvUHEVKONduGpxXsnl2NfeS1N+0sbSsSo+J5b2ikzRyWuh6tEnaf1dubYZ/US4Boyil8KpMQKuIZMRn7aUszYcI2TCHlM3Oi5ikHPMhPRDp+8yO676p2pcORm3BFEyCMD3hdOGFPyl/mwjdS+yc/52cJyV9TLXnPGQFszuFwDi3OkcPkUXO1Dw4i9xM2FcnI2bZcA/gmE0btD7/85Jez31E1uZWULefg9fbJ1jN25G0GhJmxZ8wsi2gnV8uRPY+YMCbw0g6EqfSKRbTa6NVUwTwQ5pwVQk6C5ciRffOtF2D9P16T+G9g2RSuuPMduPSXf4XLf/HneOGdb8exG96OF975dlz4woP4rfedh7d//oV4++dfiBe9O8TnL/xLPLbwDniV+FH3Oqa/kQUwLcS/J8GEyZ5nxbEQaPkgJT8R90ll6pGUfE+wcSOOlbHWIBSBDDJTE2bGCuzyVXOx2S3fKNcIL8zc5uBwVJ0JmyckpwCgEgCPHRISQJRx7MDyzDEu2S42kTp2oEDEKH6Q6t4ERpZfAFWAJnLi4M8iPomfPzkIGolnwY5q8FMZuqs2fSvxuiPo1Rtwy4+d3trZSoKLJ/4BzBiBBGHcSoIJoxw7QAAHqm6L6yeD6FYqG78VzQdXAE7B3ME88TtUBr1YGyaMbWRpd879Oh5beCecAvfFzJZ+yYqX6Bo+vpPNcgaAK06ap/8+ZX/MRDn+lCVYu3sMl/7LHXjN59bgNZ9bg/WDKrtanJi7cCE6Tliha784vgBhWCDmdX25jY0Xo5GWonJspgO7HnXx5PCTib7RqZqWA2AkgEMPaCc7IdkVCtZOpPXM4w2Ea1sYRReWyizXDsusCSOlzRDCr/TCtR1ULHGOodsFlnLMo/FxzeCYNxlqoKGRs5HjnAOUoiUlTyruXtHXgqCMu2gRAODms5MgGrUraG7NgqkAEA4LFlFvIw7A10fGcttGlGF5U/S9ShoIpeSLRxq52XFEgjD1Xgt2dzcqRp2UVk7xXhXEjmxx39V9LWLCqDHQ7dggXLQtKwxNp0YSr+02sYUoxZyJWHkWd8WpYPGh6WXDNe76ZeJ1OzmyqDV9JkxIGaryY0cmvkybCcMNJkxBgFcFGFsLfFCiAFCSG4AlignDIsCyYtYfXYi1u8cSbSml+tnQcmRyrJtMGIszLNt7U/xFLoCAeT3iuWwWjQEla0myn+fdWxVYYKCwq/3gEhDvQJgf/DaZMIrJE2UzLydHmvFaxptwJGg6csw/Y3/3Nt1u38KL0dy1BzyUddIMJsza23ahsWkx5taWavkSgKMyK8BVlihoL5gwoh83SHkWR/ex/dZ1y8iWws9UBq7nWCCEoEOBZgYI8/l1ooA4CcT8a7MQ886c0GPMtHp1jjyDGITxB4xs/Rd90KzS0bbvR8r0c2NnmTCq2Pgzbabv1WhT7+DPv/koulh8vdoVqU6DMEEbKZ/0fNsehImv36JVfaVtreNXJV6XMWEYj2BLlqZtMViSKcz0c8axbOfPkl+y4r4cmJBsLdncYgSNx4SfGkURmv4so15B0hrVOVi3TSScqPoqKqjrIYJDFDMtJ3mCMTT9vtzjKmsdd7b+uyhgusLahdOHtgCco7VeMJw5pQi2ifmkO1ie+c5jp76t9HdNKwp8R/v2I3KqiZJS1Klq0EV/vyH3npWsD6mYfjP2zFiaPZFn92wZwss+cge+/t41+OGnpgeqjO6vtW/0NM1kWz6xt31tvna+nWnE87TfAQC1NqUyw9Qc1yxj9/GsBNhTe4r7TznVTBi7R6o8hMXMzzA1/4YFLF3dHcWEscTzyCwbjXXr8tsypvuuJJ9tq4WAskwSiqrFSTiF4/mA25HZ1wJCCeP07YfPGFFqBswSiSgO4lp4uXJkcj8QEqLVF5wCECZiEZw8JgzN3+/KHxD98uM2vECmdsaOrs2AMDP2rBp5HjFhvMr0QZi+OSJ429vq1+/5OXJkaoId7s4yYfIYK8RyQMDh0BYsGUVV9RcsJ7lY2sbi6RogTEf3NEAYAwyZTgHPdjVhHNuC6yUzIiydaUwLmTAA8Nq75XkqBo1dvKjP2G+2+a6FkBjFfqV9pqeB0XkuGh0WftYRYLIVIQ6GxM/ZTfvvRU9dSCLdRb6Gn5/wAXDj85c+xHWmbhAxBERk8UYYw3BVbGDn1Jbgsi2/jciWAI6qVSHlvTgTge1zL/sgIIt4m0yYuWE/dh6qIZJFxO2olmFxzD/4EPr2/hMmbbFh7W7FkqDEM57FsvpP2hGVIIztgyDOGjKZMAIAlVneqWc0LUcWhnOhmDCkoNgCl0AZlQE8Ned5BUVPTdbSe+dIhhBt7zTOqc5py4RZObcbNy8UQWWPxW16O3ux5eCU7JfoX00FdUyxXmIhkv1zvE5UXBt+bQnmTi7L/T1mBGtth+jr11sT5z4RGJucKNTyXZyEsD0GR4JKLovHzI7GAEIWIrQFc4sgduw7PBscFiyjno2y5t1SxodH8Bwfrk1QtUWmd+h2YuQb3wIzAifRiAFEzIl0QLLZytnIyYCfGts+GRPnXwDC6KLAdpoJU8W+P/+L/O/IcbN5QQzCNAvq00SMw5dZefNYA1yBMFYzN0OZaCCRgbguCLje4KRrnAAAl2OCW0DoOuUgDKMaDCC2cb9KaqXs3HZX4rXFAFaQ3QxkmTBBGybMsZumcPWj09tgplk5bZkwzcNjwlRl4NVTvkoJCKNqvxDOBFgCwAYpzLJXYJlvO+BG8dVWTsA2IUdGiFH/ykNfNTmOaRgata3EZ4oJE9rJzfPi3bdj3oEHMW/nJ0EgmDAV6TcV6car7MgKudh4T6wl9+67N9NeyYZwwoUcmaR4+qAZbX3OYhYPt2PJVztVvwYAxoYaut4MhwBhRmsBJv0J3HDyv2Gw//9024NzztCB18gW9wUAnvxljsQKB/yeSKSLAqAkDl6o2dZ18ll0eVYpGe6q/64EJ1SQzCbZuYm0pOwmDeB1Rei64lKc9NSXEm3qHUKPktq+Boh8o4YkLnzHtPt9RE3Jkck+LfniF+OP2lVBPkp2OEyYsVqoAVkAGFieZSablpZ6C0qYhQDAUvKP7bLmTRCoHWATpfZj02XCuB1dWo6MG7UMu+r7USO3x79vxX0ZmpL1yuRrDuDgJz4p+xmhWSmXjB+rSR9DzndqbfJICIeIYzPbRcvrwc4lV+nrxCnVkmORN4FXvvOMzLEnvDn677TMl7IF9gFcuzVe35obN6L5lGA+hE4HqrL21cI3xyxAXf9rGhblzKm1+4Q0VOh2J96nlqfZe8pYS4JclawEeHQwv07DjB0dm45q0n/cvhknhmIMD25rD3oAQGQ829ORHQyaESaGi+uOmWZKtO8ebc84aEQxq7Q6t5hJAohn0AQM2uWotlLB9lKgmrMMsD3QWRx/iTiFL0GYWnUMAOBF1UIQtJlOFCqo76JMs+glqz6ShTsba9dm2lIOMFnjjUopUFuCN2kwPJYj47DdCuB14lBfcg4g4LAYcGoZS1zJQaZVNiRwuCoQ52sbTBga5ki+MpFwFVjxHtphyJUfDlmInnq2JkwZCKPAbTPelkjQmLFnzGZAmBl7Vs1+lnSJn465/vTlyFYuF5nJXWFcV8DNASdUcDEygqUahMlhCRHLwZoTku8vkT5gOt5pGcEuz6hV0a6eDQCsevAB80fbtu8xagG5Bfe0R29K5cZagzBMyyVUzzgj8z1frg0qAXSGCTNjReZK4LJuOH3rzu1GwwIOndiFjWd0YcoCHtoxApKju//zwfuwZAgYmRVLHYzMSkohXLReyp1FFJxIOTFSw5rlP9RtTjx0AYYW/L54IftCdQaXGNBL+o8Fs2WgNhV47p1iaMjiEjat5bI4ztq8HydvF8V+F04cF39gMFVcmbmnTjVxxuo9+WDZ8hlU4K7JhAnChs6Udt2UHFnKMeSsCi5BGOrmB83m94mgVSQD9Ir95xUwYfLM8bvbtuHgmgmTVxMGAE5b0otNzVWZ933b1876BcekmC2msUhDHr7bifGdk/jT/Wfg1U/8FWjlQvGBERAXdVLEeVb4uGaTKHDi/935/+K2nOv3mRWCWBy+oV+uzOFVbBzZqJObqaHfdMxssVFxZCY8McAr1Q8niuBZHlzb0rVbQCyEbidaGzfF7Q1ZqWYnUOsUcpp7fr4306c4+Cru8XoiGEc0r/g1AC6ZOsxKBjrS8mSmkV6R5LB9HgPkXWgWyCtEjKPFBTBq20yDMC5oPhNGIrk2pXH9IznW07UE5DfEeRCO0HE1CBPlBN6EdFUsR6ZqwpSBMA+1kkXebVYuMRalsubCkvoxAHD2R3+SeJ2XdaeM3nt38tjN8mxgmlfEvig7mXEtW+Uqn2GaIIxmwvBkgfukKf/KFuwQOd818thNqjA0ZwCx0NGIC8DPqSfva1BvGvOYzMCUQeHISt4nv1nHyeu/DCfYBsKFz1RJMKVzzlcxYRD3kzFBE84bB+q6MEJhW66uIeGDop4OvBgyg7CgIzlOTmHXex47gFBm6DM04ThV/MW3HsXDFR8gwA9PeADLd/wUAPDUSW/G9p+IYOfKvcCILdgEk8NiPjyueqs8JwYvnMD4jg6jzhPBebNEnxV74nCkaF99T3FAPaAMZx3ciOW/fTUm77gDHRKEcUh2biJSqslmAYjNMXXHXZh3MJldzeUcrVgw4BFs09+3XeDifMmmo2k8JUfWdUkM4A194fOF3wv27D1qmbHNw6gJ4yIG7gDA7Sr3DzJMmDbHzzJhykEYE6RpB8K0Ur9tk2bmPWURj7S/Fe6fRLdMJOBRci8VktiPmHQYjp0j1gR1HV219hGC+gNiD8ciimZF+C/LjrW03ztSjeVc5/fK4yoQRs1jPISrQBjLwz0XfRRbj70Wt31X+gSMoSnrs4RdB7H4hFl4yycuwQNnfQY7u0U9wkk3BmHSzFpldYvg1OG4pkvz8cf1fDfZvQQA0HAmAbeCR48RQCKzfQTu9ApK5zHfa/eINSzwUiCM7WcYO4oJY1WzvkjROc3Ys2dP7puAeQc3bh0pbKssNNbErY+UA2ucc3zt/WvwtfetwdCe4tpj+tgG1aodaxgAGnS3/ttbUMJoAHDoP/4jMe+1mZYw1Uz2t9EqAZIMNok+flgcrA8p14ls50o1GD+qIioATCfCscTrWbRYxplzDiiQ3RYyyer5b23Jsl6pEd5uePL55bImZFqGUvuQFK5XBbwu3HFyts8WBwb7M29rIzmJbuIHpB8pPz5k2xqEeeetf5FJpuJc+tMEWo7MocBdO2/DwXpybIYshEulHBmjcIyaMHkWDccAH7FtnXA9A8I8O/a8iYB/5CMfwUUXXYSOjg709fVN6zucc3zwgx/EwoULUa1Wcfnll+PJJ59s/8UZO2p23NlzE68d/3kzBA9LjmzO3I7Me56X3UAofMPMttea+3lMGM7xr68y+tFh1HBIBUJM/Wy3Ymxk2vQdAKyK4WxOAyg7YX7syKZ1PpWleQeKCQPONLAy8Md/lPmeraTrlLLCDBNmxgrsxAUiU/KRfjFYVp4/D709cWBFZRmbWcymv7S3KTTHTemFwbnn6nZbV7wMZw19BAd3TiCIGOrSgYlIDbMWJoOkrQ6hBa+CjfEGPnbCH6mLzWw60Hxx0wWVTpRNp7Qc2SdflXwWO2tiI33MyOnxmwbwYR2rwBnpZBqZn0TJfEgQpnrGC0Tv5G+dv+vl+PYDu1BrRQjD2FH3UnJk/WMbxfutcXm+FUCCU+kNrrLZMnhOZdBKAc9+zhxZZE7H7Nz3u4yNOee8LROmp+Kixbq1PBUAPDnvHlScCu7dIhxWNV5y9zeVHkSEwGI2Rr5awe2fi2s+8IoogEnH4k0VN+TIbCvSNXXURmrN/jXxsSmNN0FWAFLphhPVkdabr4ZV2AfWg+nClvHnOrirkDjjq6ofTS+Ca7sxgM6ENEPg9SDcHRdDNlkxxInXFDqZDdhN3n6H+EyOp3G5rrVlwpDkfUoDlKap2Dy1GGwZQAo+/5ncthFlIDLb2OFTMQhD8ot1ejKT0o6o3qiUAitGTQ0/CGImTCsnmEkpuLzfxIKuCcNLCpN2ptbh7iYQRsXASpgCUV4+MQ5svaOwfdp23nXztNtGjXIQxtuzO/HaAimUFYkogyMZEJ4GYYqPzQ1Gn2LCOACm8thZgAaTbcdGaMf3tF7Pu08SuGQReKORYP25IRKMktptcZa6L9mIhBA4HKBWfkA7soG5ZBKubcF3LMQJlDmbeQ2qxIFYIuuAWTlbOSXTxgiDY3uAzJz3OMdkKtNXMGGU3joBkYxExboz7aktI5hsyEx4yYTZM9rAm8bijOOF+2Nmzt3RJTgw5yylvInRAyJoWe12cXnfZ/HDk/8NU+G/odoUgbKwYetrE3QsBmCAMDl13Irs/I3F0aggYvjIvf8FEoXY82dvQ7dMImru78bvPvxBXLDjFbqtLddMiwYg7xDMQQKOzlqSzdPyejUI4/B6tq9dYr3i0z+FX91UcCnHhw+25Es8jv/4x9j6ohdhw6mnHZVivWYArBgoFTaQ8geKAHZlgZ/0wdqBMGlGHi0BwYGYzQy0B2GaKbaZ6+4rlBqkLAZheK2BCx9QsofJvRpF/Dwuj5rwJWNNPR/ueHI9GP7yl0Ep0yBMT7+NN535cXz+wr/Ed07/mG7XUxW/x6NITDlybbqVnQ1bgjCmjOjme/br9o2qGNfBLDHHV7pctLqHMeEJv2HCn6+/Fx2IQWzTGqlnZfLW2/TmeKJbMIrH3Qk0AoaRvnUIJVNoyzHX5h4vbc3JLAOHy/UnDeTkgTCKCUP8rC8y/D//M60+zNgzZ/N6KuDG4nn3/65v+x0ThMllL6faNqfEM7N341j7YxvzRhEbzjTL8I3CNmD41H0PJBJlaZHfI63WTD4LrZL2nPGMv97IYW7o3zakx2bvEHWVeltzMD6aL/U2FSZZSnt67y889uStt+q+WLY4nlIJsLqyYCw15DAiItpbXOxF0v6+mcgjmDAdaDnp8xRMmG3zp7GAFzBhbMIBtwNP+Z5W6zgwuQ+NVLKLL9cCRoCDS18CQIAw26Z24+/v+ftE25CFMaueR7CVUkIBE8YEYcQB4rl/xp55e95EwIMgwOte9zr82Z/92bS/8/GPfxyf+tSn8JnPfAYPPvgg5s+fj6uuugqTk+2R6xk7Ouak2CBuURHS56C5hhxZO23ivi4v4QSEVgAnj9kiJ+tOZoIwxXJkCMQCuv904YR2zDNoqOeckWhqdcYZSG41P/A1HZvOxrfTYAl1FzBtbCjdTRmgyGHCWNUs3TuUwT4tR/YsFRSdsee+nbJIMM/WWSHe8Pfn4YW/f6Le+J6+uA9nLxPB/1bEtKNkjm4lNWaCIgfmi0D6RM8K7Fz2EoReD7770YfQihiolEeJrCn8w4vel+iLCsqqjL540x47Ow1XrEWBl5TZWEgtRJDSUiyWIxvqST6LfpB0YgmjCd3qSopZ0r/FaJ8CYTovviTR9tiRM/C9bz6CL9+7A1EjdtydlByZGzVw6S//Chfe/w/GoUUwhBTIofky6BXZSTky/3CYMFZ+275KH5bI7ElCSFsmjAIpdvdu1O/dt/QGRNTBTx8XwYauioO3XnaMHjMW4yDrfyTOgUUYsW3Mri3KHtw9HtRyERmAODfkyBwr0oHdPHCCU6YBQU5CKYsFOCmpqUrUCevQU+D6niblyABgL6QkiVE8ks+WWax2KJkwBDvYQkxUxgAALa8HzAD3qSEr1W4eZlKcWoEwByHGBLecRA00bTLDjlvJ+1TGhFFYVGTFcmQYyc96DCkH5NrjkRa41Lp3eH6NKKruEY30GqgAs1YruxFViRTMAhxKNcMpymvLmM42tuwYhEHJPuh0iLWx1WVIha59oqg5ohTzZR6lwO4HClpnjX7wk9Num9bQT1vnts0AoO+RzfOzkwEBlLtyqtSSTiUBT2YWVTUKsI43ioIXkuFkEUQ20d/JZcKwWI6MT9VgGywyAuCra+Kiuc0779R/O4ZslguCXf1CWoen6olQi2C/ZIgRQrR8ZOw7Gs+JXEcU8GL+beXI0lEtGcawvrYH1BHjvZP6mEjJw4FSXZcLNtFJMX1T2fvawwi2HpKFcdGEY3sYb4TobcWBkEprDIv2/kK/fvLktyCyfficYHRQBERmLeyE07MEgz3b0FEzAAEWP0dB1woAcfaw6YsyI7vYX7kSAHDzWfHnZbr66UShlYdEFv5J209Hd9CPM/ZfCQCoh3V4kWJDBSCdMfCfZiSO9x6DyBHJV66bE3R6NpSXFTVyGmx2Zfve/Tf676nbby9pefgmkiLiZ6DeBiRZOSsJqgyPZ0HBxPEVy03JRrYLeMo5qIV0oky+HQ4Tpp56xixQBBHLBbYiTrUcmcUjdGpJzeSgYSReix2EWvpZMWzi4K343sHrPpaQI+vud+CoJCDCQQLh80SRYt5E2hcDgBZ38XhVtNcsL9FhHNgxATCGhmTCcD+WKn0yGEHLEet/ZLDL9rzznZlzB4ApY68KAFN33qnTxqe6hF81ajfQCCk2+w529It1r9aV43Pl2MR3vpt9k6rzSiZMUtvTdayUpZkwi//zs/qz2t33TKsPM/bMWsVAu5tD7ev2TI3GbdqphZhM6HZzBgBEJhOmzZwHQNeHms7xM0yVoDyYXk+BMGFBnSYACTkyXYuyYxvGiiR/VR1TJ0Jvf/xcrfnuttz2QUpSOq/enbLJe+/TrHrbEvtYtTeye7IylUQnsjBwS5xzf1OwRTIgjOFDOl4V8DoRugwkVTfNYgIMAYDB47KJ1qSoxrW8/xY4cNYfICJEM2HsHFlhKplD57SaaM49GYAuH4t79iXnm5CGWobTSteEaZNDMVQf0n9P3HRTScsZO1r2vAFhPvShD+Fd73oXTj311Gm155zj05/+NN73vvfh1a9+NU455RR85StfQb1exze+8Y2j3NsZKzI7Bbo402BlPFfMZJCMDpZTkCuuA3P6DmHnghmOvB5VagTpSuTIIvnZcFU630ax73QR8d7OudqF752zWL+/+cH8bKRCm+YG7t9/+0y86QXL8coz8h1jW8rhWPDlvzEIoyXGcq7R2mNU4FO8tmaYMDNWYEoWrxkxdM2twnYs7fT6rqWDXE/uM8GLeMwpR8fcmM0eEoX/JruWJH6rFTEwItpFpIbj+o7Dm//tUuzsk2xLRZ1W2fo0KydTd0U/Wl4sW6g/48cCACxDjoylHg/FhNFnwiMQg6nia4kb8dv+hOH06k2KdEAJQS2VPPMW9gTGDu4FbcXXyzGO33neaeI92oLNQi3XxCwZPCkAYVSGe5SqCZMGjcqsCIQBgJce81IAQkO3JXWHi5gwym5bdDsOde7GfUt/jNBpYaIWe7BvuXgFfv+CZWDmWPnBmwHOcdfQ4wCA/sYC/dmX+mLn9p4LP4LQ0DrmjGoGioX4mkVONZsmzajO9oIVAFd/GAB0tpOyJcO9iEBiEMagDiwbEPeCKtDRcMxVthglIapOFb5jo+5NohqJwMpU15KECDcN4vHjWBUs2it03HlO7R9v+QowYmm20wESB3FafdlNk5bOIUkgvowJEyn2i8N0IJoXrFeUMRAmxq6HJoiWI8tnwqi6RqNLYjaXAswaufJbMoCeqqG2e8N4TmfimjCWTWAp0Cz9gBvG1IbQSM7gB4vX8jsaexKvIxCd7DAtO3Bo2k1ZoxyEaXUJ8NuW4KGFYimiiHENM3TNEhOSVxK3SMiRUXVPSeHxFePDdi2ctoPDkoGAdNAUMOXIJHjE4uADAzA8FY8DckZcgNox5jGXWHhq3j247bivgox/KHmuFnRdMQApSTLA3EVzHciJ518NyOzPFqeN1HpDGO4efhxBRczhLrMz18ZkwsC2AMWEyZEj62EEEVNgUROO5WKkFuA9wZ8m2q3a/B1ccP8H9evBeefhXHRiZK8EYeZ3wl55DYCkzrqKR1ACeHLuUNnDCeDXmJfcRcLn3DHPmJ9LxkyUyvr8/bu+CgDodGKpDy+qIqABqrouUAB0DMSft5LP9ZMn/qEOVLs5U1ZRYfSjaZol9jQlfBtPFIO8T8cCyhI1HSZznjnTSKpmS5ksIQAwBbDKeSBsE5BU4G6LpBNlCponQJjy4GhadkjNIXngM6OhZsJYLMokWah5gFrG+x092p/VTLEc2VkWhmhoJowLy7JxrgQVFNi7Z1LIiQoQJp67GtxDqMAUJ7kub3pgECyiqHdKpou9K/E5z6Ev0kNDmfcAoNUcASMWhgZOQeBK31E+d01fAEjjllinX7HipXhgqZDOnOpcqH2ptO01yuBEObVoaveKYGboJoE+avuwBwYS77GmZMLImjDdL3yh/qznZS/N/f0Ze3atv8SXShtPzyttGIBmTcB28wCABOu3iA1nmmOAMFFbECY5/jPSrylrpkCYoGyOZAYII30Bx57AgYl8UIuFUl7aDtFx1V/r9ycLQLAwpwZMoUwt4r2AZ40BAFoyITCPxaH8CJuFgASvqe3jhNFdmTmYGbX/HL8KuJ0IHcBiSbDJ4nFyR59XLhMX94PBasjjuADcKl45OQVPLn0OFYl8pnmSEe9aHLacc2zjtprXKMuEEedqwSqUJFP2rjveqf+e+PFPihuq340iHPrP/8TgR/4Ze//fu8Fa7cHNGSu3X9to5vbt2zE4OIgXv/jF+j3f93HZZZfh3nvvxVvf+tbc77VaLbSMgTUxITYuYRi2pQXOWHtLJ89y0PbZSs8RMxKIMWdJV9vxwC2iY2EhrPz2cka3eAV/+4c2rlrLwF0bNgcoo5nv3NoSEgjrKj5OAUMwES/W42Et0b5CKvj6Nf+Hil1BV1fSgT6csczAp9X+JSfNwUtOmgMUtLd5EoQxmTCUi++YGSP6ewp8UYwCcnj9n7Gja+pePBfuScWOnZP1e8dw8sIeNKST6RJgUjpbo7UWuBWPNdX3s+wBAAcTIIzKNEpv1iZrTXBLZP+Gdg00orAJx13HfBu//8iHwS0P95/zXiyIxNpB1aZcbsbDMERntwVKKGzEk8uUN4quIBaeJXQKoCLj5VXHvwrA9/RndkpyyGIUzHX1+TipzCJCjWdTBfxJ/F5nSwRKqQRHQu7j9dvfj6dW/i4AsdlmRvv573srtr7y7fHxWROwPc1mIDbJnws8WXxZM2FkvZmC9oAAUZoG+GDDLmzbCMSG4dsbv63fc4lb2P6S4wZwz+5X4PunfUK/NzIlfmtBbwVL++ScFZ8pACAMmpgMZFCxLkCYUy5fiDn7bQxNNjGbuojcTuyd6MVC+dssimvC2CRCpSWYG8z2UA270PAm42eq1YprwpAQ0TKRoe1EDZh33uGdaNFDAoSxROA1vs8cFjHj+/E1pqoIthXBhQvPIaB2UzvvW4+9FufSId2+NlkHZokAS9Pz0T+2CXsXXYpGT3atohZBKKU+OBimeOxu2lMUQasFYoACWuqMVAAO2KQFyn0N1KWPz+p1nck82UE1y4KR/HERREyzML2BJcChGISpNYOc70h5GCu+XirIP1WvZdprJgwh2HbCKdg7/zIAwIFd9WzfjRocovCm6DuNitdaIuUyHEOSp0XrhfPv4ym97S/09+LP7ArYYczT9W3b4C5Z0rYdazRL5/9av5ChtWkLkdsJG0CtESCsZIPDjUag60DY//g3wGlvg8uK15ckE0bcH4ej4J5C16uyZWBaja/J8Zxz4EqOLL7mcw6txaE5Z8DnQCOI9HfY4qWAwqqt2N9zQcAJx+Y5D+MKJAMk1AYsy9FtVVA1rv8Sjz0FOJLEtk3W3oqy+5NQMssYYXjzilfi4D6mv5G+NrTZ0uw8bhGgPwmaAUCnNYwaG0AXJyDEA4fIzN87ItoEtBdpLl9H4xBW7v4xNi15OTavuAQunsL+rWMAgL4FVdBQgLE/Oc/CH/1cyqdRNScBFTlPNuV1psZ9CIMQlqO+I943MWyLCwUFBbiY5+vWkmoI/qAALF0rTq5aOnoSxupT8CTgRBAipBSVs85E85FHUWmNJo7BLQehYsLMmp195nVQvniNO9IWGRn9eb/Zrh/sCO97J1PstPF6+fFJSnEgCrLrjGkaYKUthOhGuHc/wnBlbltGud6ftQgALsZU2fEjI8BJKSvv+333Jl6fspsBZ3FMNRRLJ/4upzUDhAnRlSOfBcQFpgHAIgE8mahXb4nnuXXGAmA7EslsLAg0E6baY4NYNl4/OYUHzfomTJxL1GzpgB4ALOjpw24p/QNiCVlG+Uw+dvseLLmYgtpVgAXgi2Ylz0kBW23GO2EcTUJwYO45WH/iGwEAL7zz7QglsF+virloBB6mmgGuPnk1frTtp3DCKURuF6Y6F6BnKpa7DNxODM47Dz85/VG87adj4lq3WtnnUc2rGSaMD1Srifb1+4QMIXPiubrnta/FxPe+B9LV/ZzY+/y6WJE/Y5EYgx2aqKO3RN2Dc47uFAhTdo+CFAs2MNb1PGsYyRdBq7wtkJRFrLfaz6lJEKb8+GkmzEQt3+9R1qrvBhAjlGHJuYZBqI/vhVMI/D74jGBkqoEwzGYaOFICq0YD3L27haYzhUrUhb5lXu5vNFNyurNrs1FrBrEfZBilTPtqrj2OpnHuYY7/qRLSLBrAhpTcIg4qNMBUIzkfREp2lnPA9hAyjsAR6wg1wGeLx8kdreah7J7HAPG1X1irwZJrf1glYDvuwclBgENyWn3pgwyNoIEuO2YSu9QCQHFiGMCWYI+ZVFJv1eHJ2pqNVj2XCQMAJw9egrWLbk2eq5FIZ6L1rU2b2o7L8W9/G0P//h9xP09Yhf43vrH0O7+Jdjjrwa8tCDM4KAo8zZs3L/H+vHnzsHPnzryvAAA++tGP4kMf+lDm/Z///Ofo6MjSz2bs8Gx8lweoILzNcdPzjgInshYHJ3fgxhs3l7Z0WZzhGBGOG2+8MdMmahAAXQi5j+0LCL64wMYf3ycWoDvvvAN2JT8r4PRt0sEdjx/hzfdvwQ6SP7bXYz28/iqCUQdzX1DL7Uva1NZl2/bteGAa7dva1CjQCcy3lsJpLReZhRDBkwcefBD1sTG4w8NYkfraa+5huOFCSzNhNm/Zij1Hoj8zdkTtlltueba7IE08E7fddTd29gC7Bm0ABJueXAeh3mCj2WhgWGrQc0A/D42aCMKYG7NWp3JYk079XT9bgz7J+AisKX2MmhezRmpdi3D/Aw+i2Wiga2gE8GImzI033ohXk/NxoDqI2fWYPfazVV/Cax6Ps4js6IA+NotitgWQBWEIp9i1b59uf6ABmCJjnBrzkCqAjEi/txLAwPATODjvHADAIasDL28+hp/tOg9EzggPPPwwmqPi2g1MbYCZM0h4A0DMchibmsidaya3uwAqGSbM2kfXYuvuxzLtAaCLd6FpaKKPDI0UzmOb6psy7z312FOwN+RnBI+PWGCt+Yn3vnPLvQBsIGzo3+nUQRAxFm6+8SfYPr4VIEBXSwQL9hzYhmaN4XudFv5UguQ7hhwckseYNX4QfJZiwkSwOIMT1hG5HfCjDjS8Sf177vAwKnKjcdCl+Pk9D+I4ZGs1hE4n1u/cDUaXCBAGNHFtHGLHAUoWj/du6dhTK8QtN9+CsSEbMFwmi4V4bN06TPhize4fG5cgDMXeSkPX4Aibrcy9qG7fjtmqYLUVIWKd4AhB4IJZLu6+7jpMnnGGbr9w7x50AQKEAWBbU6DU15J16eM7Y+M66DZV4TpQzomdOy4GD9qYJ0GY8cgBkVnFHid44JG1sPY8mvqGGJPNSJzbSsRMmEceeRR7DqZ1xkX7CAyNMEL/yHqMzjoxt+9LR0fB3eUAgL2jNcyVm8SpKCgc0xfcJJ512whM7t2/U8+705l/b9ywFmwo//h5ocq7br4ZrUVZZmu67fj+faU+RdCKACcGD20O/Py2OzA/x6V+/CBBvxgJcCMxH9tg+OlPb8wjymJ8vII5cEF4zGpxADy89nF0HsiZS+SYmZgUTAa1yV73400YGV2baNqsN+BCZLFPrVqFro0bNQvN4wQ3r9uFxoEduHQBR2XfAaAL4Jzh8cefxNAWWfOHQWsOpJNzqSWy6/X8Hoi1ijIG2ACl8dw8d89e9AEgCahD/L1z63ZMDiev/4YdNuagA5wwOHsY6kytFTZuXfMo+K7Yt7RqNT0HN8MQP7v3XqwE4FCDCeOMA8EsWCCABFcpqePWO58E4IJF8bzfdIGK3IfO2/kLbFl4NZi7CH27L8Tg3gnZv3XY2y3qpk0a40DVRWIWMLhnH4D5uHebmOvrB7cBEPXPbrrpJl3rZNHgIDpT19emwCdv+CRO8k4CkHw+vJFs4eU/WTAG+2B8gBdt+QN85+Zb4em6HOLZ7FlxDOY/8ii6J5N1jjgYbjx/PlZMAFPN7LrX3LkegJA2nY7/fSSsc/16LAIwPjmJx+Vvzjv7LPQ+/EhhP8xne9v6Dbj/CPZ1uAmYYYexyanyazHhw2R+jY2Ml7c3QBgAYA/fjxtvzK9HIJYusUdTTJhHHnoUG/YXZ4YPD3VAAZ+1qXppX1bema2pdfLIDtxyO0WvlxyPk0O70aOLK0fwwzoYWKwWINtZhr9HmkMYax4CYOGhRx9DZf86jDAFIopvTJ5yMg6NRgjm9AEAHlv/ELb6l8Pnj8hm4rzHx8R17X50LQZMJkyTwumK1xueyrq//5FhAF2wo0MYGovH/ApnBZhMAGIcCf8ybS4FDjq2YN1Kq3XMx313343FIIhcwRIfJlWs37QVA40DAAE66gcx0duFVqUfMECYPYsuw47lL8UJtdeCWn8Jm0XYuX0ntqTu1fzePvRgJ4KqmLdq7jg6w15Q28emteswLNtXdu/GUvmddXfdhXFZH2buXjEfb964QbedsSNnaX+mYtmoy7XhOz+5BYs6874lrF6z4fMkSFD2rKo4jLLH1j6ObaOPFLZvjVqAlNfdvGkrDt34VHFnAGzZZkE5AU9t2IwbGxtL2zssnvMmxidL+744BcLsGxwqbU9Gd0KtoQAwOly8j6rs2AFmrQIgmZ9di9HTquKG2+/DwXnZ2JQdyIQpEuFn63bjwIK9OGv3tdi5bQ9uvHFPpv2efXvN7SLGKqP48Y03oyMnMt29azdYRexmXVvWH5VSh488cD+mUpK4YSsAPDGf2kQCIpYDDoJf3L0G+3rj/o+McAA9IJzisSe3ol67EauiUEvVih/jCTkyEJa5bsEOtcYT/ZlVr0NVZiUWwPc+AqOcJs7bxHHLbbegz+rT781TdZAJx+YdO7AKsUoHANx4843w5LnvC3eDW8LHITxKRCl6G0Lm0eynv2cPlsm/J1P+dzu/ZM7tt6PfeL35wYcwNGdOYfvfVKvXy5WSTHtWQZgPfvCDuYCHaQ8++CDOOeecp/0baQkoznlpjYv3vOc9+Ku/+iv9emJiAkuWLMGLX/xi9OToDs7Y4dk6fw/u3yr0l/2qh9WrVz/LPTo8Gz5tCvu2jOPkSxfm12wx7Is3/VL/TS3knmurHuIrd94HcAsWs8EsqgvWX/miK9FhFBUHgPd/4/0AgFN2ZRfAV770laX9YVczNGtR5phFtuVv/w4AcMyxx+G8I3Cf1vxsK/YwYKBnPi7u+iCeGBXnQsBw3oUXouO88xDu3YudH/+XxPfUZl4p3pxw4kk4+Xk2bn6dLQxD3HLLLbjqqqvguk+/9tCRss9tX4MNg5M485zzcMlxs/HpTXcDU3VccfH56O/w8Ln19yKyPMyePYCxcQAgOO+K83DND69Bv5I3cuPsl7BPBCF5at04vnsWDkgmTODU9PP9/m+8Hz894XN46QZRv+yYY87E8tVXYN2d64BGDMKsXr0aT22z8c11OwHEgc4z2Wb8S18Df1u7FSufmEDI63ixMd63/EOciULAYUcNHUi0eIRlp5yK1asvBQDsHWug8UGZ4QOAWJbu5w1fEUECyyb6vS99/wOYPREHIUak3FpPfw8mIUCeiy65GJXThRNPdvZi6PjPYnSz2JDY0SQiL47iD8weyJ33Ntw7iLs2bI6lpmQA8IILzseylflO3cqxlXj9ja/XrxfMW4DVl+XPA8tHlmPNzWsS711wzgW4bPFlue1vmXoMj40MJt6jfUsB7EVHZxdWr34BAOCJ9aKmRlfteEx1zMI1V12JH/3yF8BBoDMQwYJzLzoTP35kO3ZMjQI8BIiLSXcvXrf6z8VvfervdD0QsTmw4EQChOlpDWCs44C+Zs1t23H/18R5UCvENS+/Flve+/eJDHUAiNwOYOkAyD1xsXfzuv/lmp+DWqoGUny/f/nvNwAAmBXipS99Ke5oPI5tAfDQ4ptwzp6XYM7BR3HqFaegV7Zf+1/fAiCYNr+z9DyACQmSzohl7nPjwYew6SvXi+OTCJFVAUgIcBfU9nBSZycGjO/s++mNqAMahHGdKQR0QI/t9PHDffvw/Ztvkf2JmTDcsnPH3P/svg+kJta+JcuW4cATIkDvcYJVJ56M1ecvTbT/4k9/DgDwuzuwevVqbPnbv4MrJRmWLTsGV1x9RqL9t771JXF9XRed3d1YtO1GjM46EdVOkunPri/9L/aEYgxMRhyLWAsNAL11p9AnUuuxabNn9eCqq67KnX8/9rX3YjJVkPuu/lH882Ec/wUveAEqJ52UeI9Tiq2ptsc2RrF6dT67HAC++cP/BRCDhzaA8y+6GCcvzJGle3Av9j68DeBMy2E5nOPKF1+dI9cFbHniQWC0CcKpZsK4nODYVSdg9cXplA7gy9d/FwAwd+7cxPtO6GH16ssT7217+IsAxLy37OUvx/DGjVoq6LKmi4uGRvFvjW781esvw+AdD2FLEwAYzjnnfJy+TEjWfOHb/wzITMu02iC1gI5qp77nn9t2Lw41p2BJhphtx+Nhz+13orkOAHF1BiORAeFjjj8OF52bvK/jN2/F5Pp9YIThzLPOwT27bsVKAIf8EH0LV2D16hN022hoGD/4kZgL/M4KVq9ejR0feX9Cjmy7a2MFr6EWdoE7YivOSBOzl58MbNiEhT1dUPySf3mNhQ98SxYLjxqYd/BB7F/wAszbK4rMzjumBy//7YuFD/2NjyXAExbFTJizTjoRt8p41cnnXw4MzsLPHhYO4TUvfjFsKY259/rr0UAShHEo0LGY46rTs8/HD276T6Ttz1bOwv8OxQeY9Idx72QPLpLp1w6bxItXrwa78kps+/730TeeTMIisDDQEpJ08xbPwzWrL0x8vumXNu6Usbpnat8z5VcwiK+gb2AAp8jfpBdcgO2XXQ4AeMk11yTYiEByHlg2fz7OPYJ9/cCPngIQB+Ns1888c6b9/D6xhwoJh8sJujq6sHr1FYXtv/oDsd5oXXyneI/ZmAzwtVtEIehA3vZTTz0Nqy6Yn9seAL7/2CMYHhegju9VsHp1vj8B5M+nHWET5190MTY8dHdiPP7kK/8FMhWDMAQcod2ET5NRshetDfGkVGS3CMWyxQvx2Mggjlt1Ila/YDl+fnAvdhh5ePPnz8fa+ov06xdefTkIsfDAl0S9Lw4B13R1d2P16tWYCEPsul74hZREsDp6cUpOTTNlQ3UZzmNTWLBgEVZfIa61v9vH17dIP5XE4z3vmriRhTpbjkozlirbvfgKXHHWWdjlfFP7hkPoxPzFS/HGl1+Dvk1VjN6eBJwAwF2yBKN9q/TrQ7PPwPyDD2HW3H6cnRoH+2+9DTUAzomnA8PAZGUYnWEvWn4fVszqx/mqz6eepr9z6umnoUe+f+jxJzC+Zg2WEqLbztivbkX7yb958FaozLG5K8/E6tMXFBwB+MKONfD2x+PWWljF6tWXFLYf2V/D9+6MQZcTTzgJp16RTT5Rtmf9KG68T0g1Ll+6HBetPrb0nO694Sn07tuLLk6weNnSxNqbZxtu+7H+u1KplM6Raz/8b4nXfX2zcE3JeLx32/2J1x0dXVi9+qrctvWHHsKWpySIzYUvUG12om/xcVh91fGZ9t/5VyFpFRAGp3sAUV0s3n1ds7F69XmZ9j/83jewzZheIpvi4suuwPzebG3gzfevw3pZ7tGxRSIHlwDUmaeciu7UOd9xy1eBSEiKEUuygi0HHMAZZ5+Dy4x95s+23gUMioTg8y64GB1nXYzBNe/SzHdlpzZb2EZkEnlOTG/zLy2o3Cz1GR0ZwfYPCRlp17ZhVbrhNMcS37vkskt0HVMAWPsP7xHXo2cRTjvhHLR+8F3YBsvmyquuRLcnEgge2/sg7rtVyvzKRLRq4xAa1TkYr4pkkxdc+QL0+mJ/2ly3Dnv+4zMAgH0DSYe0zC/hnGf8/mOOOeaIxAZ/3UwpaE3HnlUQ5h3veAfe8IY3lLZZvnz50zr2/PnCkRocHMSCBfFkffDgwQw7xjTf9+H7WR0913WfEwHG57t198XZCa5vP++u6fwV/Zi/or99QwATiyvo2SMmx6ZNcs/V6oyDCufVOO7rIiAyY8L3vcLrY2YaKmt7LV3Ar2THdjvz5887IvdJSSNFzMIHXnYy3vC1WI7M9eS5esUAkWLCuJ7/vBs3vwn2XJkjq7LO0lP7p/DCExdg+7DYrM3qqmJWpxhfE80QpCt2QP77yf8G4wx90t8MvDjNKmxSuIvigrzKGiMRqKQQt+xa4tx392+AHRwC9eaA8giu6xpF06U8muvCd31EVjLz8hcdFczFMGznFizcTzG0uLP0unrBJBoyUE0Yhd3Tq9t3VRnM/KDaXOPZkWAvrHju6DlzEaw7zPolYr5oyLoqhFE4lWp8jOUXoXdZXYMwXjCGlhE3sP38MVGR90ExYVRR6Eql+Nk+cc6JyfN2iufHU+acknmvw+8obN/hZ12hUAbfLl05R39vuPNY9MnPfz7213i1TcCf+gEwewCdgfikd3YnOnz5O8GTgH8GRsIpfQyLxRmlluMAYAJEATB3ail29T+F0XAUczvmojY5pWvCRFaoj5FmwkROBz614za8jb1WvGEl14MFvRWEgaV/X33GuHhWGBHH7vAdBLteiponpHao48O2jHVa0uo5GPq65mCCy4KbLLv+BBbR58msCHCrYuxzISNgOU7iO0TLLYmxHLkMaAGRXcGuC5bixNTxuW3r7H1GmNbc5yTfr2hFVGcYdnR1oUIaoAA6ESBkOeuzBMq4MYbdUISZW7Uo5zdkX2wHru3F9UMIz7QlRg0OSoCOcYaJucDCken5RIcW2pizj2KyNR73LTX/dnGGSVhwYCGSwYs1tV2HNUePfPJTWPbVryTeY0Yh10m3iu6wAc6y52ga56pWg5hHHMZBke8TKcUpm4WaeeTAAoWV2z6Wx2Zxew6ENHt8xrgO6nnS1+gb24SxvpXoP7k/055LYXHCKNwuMdebwIQbzcVLa09h7/iFmlnIQeH73fF9sVylNpdhwkQ2EFp13bbiyXnIaBfP10p2zegjUZJ5OddGXpdKQGE/sBnUEtfGZi4YUs+rZelnaRcZhOu6IBbJ1ISphVJe0BXgGSVNXHezYB0un92Dx2W7J5YnT3TJnjuxb8GF2re95o9PgW/UFts5N24fNeRzYQEnLhgABDSLRgR0uT4gWWOu48CW59C4TwSWEnJkAKLJ/bnPR089W6dp6J8/Clx2kX7d3RrAhgOP4TIu9osunxTfV8eLGli282ZM9CxH36xhbOcvQE9L+AmzFgxk7oflxL7+M+UnqV+0jHO3qnGAyyUEpKwvrdYR7etT+5MycM2IlR5fSRG3bAI3AkCL5xnOskWki9YCAGhIqcEQHFSXx8ufY5SZNWPazXn5xkHlXGiOR8JDOFKOjPAI2+YDkRXEIIx89juMGjoEIXo7xDNUC8R1nON1YgdiaczarbchvPzV+jse4UD3bHipuguW7I+NWNKNE4o5fT142Z4JfDV1FpPd96F78oK4L2wKruPhe4/ux08e2weOLr1h4wXztrJz9rwcJw5diX0L9ur3mOWATEwglH5RaDUREQtrto3AdV289uTfxfvn/jcWTCbr33Scfx4qT45APd2Nqgi09v7wejgf/iCIsb9kks3dlMXEB7u3Y/7kMQi8brCx8dw+97/iFbDU+5Mi0Fa79bbnxL7n183M54MxnqjjwQp8B/1d24JnLKLcKm/PwtTCzNu0N+tz5Pi+aaMc+JNJsdc5MJHnOyZNSRMCwEir3GdLy5E1w/I5mzEr9bp4DiawjJrAEoSJGKKCc7YjWcONAE/sa6BntiznUKO57Ylk586iFCNSHnaKNuG62eQcapQwdywlkeiCg8Di2bk4kkwRi4VajoxZDkAIotT95SwC4IJwBr+zR3xm80QNQALg7EaAnSo+bGV/0zPqFevPiJIpFvUBidsB2wBhHltOcK6dvJ5q3XMcB9WOClpIypERI55o1sMhck2bPfQ4di95ITx1P3gTs13hm4SyP/tzwphl46b+SJYZZtvlc/tvqh3ONXnmKwUaNnv2bJxwwgml/1Uq5cV0i2zFihWYP39+gtIYBAF+8Ytf4KKLLir55owdTfM74sGpitL/ulrvQDx2WwWnajuWZtR8+NCUZsEAYqItstYzMO8t+tdPoffaa9HXBiidrjky+BxRgrk9lfhcOdOFQ3VxZgADqbpNigljOb+2KoozdgRsXOqO7xyuY8jQ7l3QW9E6wozrmqwAIXjkoHAwlKPTrMRZMpwDC/7iDdCRMS6OP3mQIfRENnXNHc7pifCk1g+L1JhaJHVpQf8/e+8db9tVVg2POVfde59+bjm3t5Sb3hNCIAm9g6FXRRBUUEFEQP1EUFERpYgiIPiCvvKCoPReEyAkIQnphbTb+z397LLWmnN+f8y62j7nhiQEOM/vl9x99l577bnaLM94xhhYN7Be/TTFlqnTct/KAFw2+F9I1UKZev3v9yC1+uEeT0CdMTMOPNwztNa2yKmM155MrlM7Hx8DdUwCCY+xm69EO7PSgcRJJsEPER+3Fq01EhQYmp/OtY1WVK4DQNiQx5SVPGGW/mz7NYasAOAVzccARF49AB2pYzpn8CUAgH+85B8xq+6jk9bYBYFYazveqWw9wDKkIIAAmqmsTGoOh2jo41a3jA/3vNuFU3LxmwAAjY6sAuXKoH3nrCxnnXz3P4Kpbdep/nPworOMJJIbF9+2Al4mfydp5I//zA0j6IYahCHo3X03hBDG1FYoIDDyPfSmHo3Uk/vPvNg6XAIQaiHMCYfXHDL3iqiaSjpJMUEyUD82v8NpkM+ewPb9FPKeOKCeQebH4BUmv+A8B8K4njBVkbDUaG2HrWEERC4qx0WvxsRdtT1yFkeqMq5XZSitF1s0gOeHBhCoMlblSc+cGw5iNKMFWdrAvnKfYuxdVZbdkzvlSNTNlznnrleQL1ws2tdcU951xybmf7TmFACA1+1Pv9cgjGaR+BDo1XgBZkpujbLEnENPkJprBAN+UMHN9QkAdLPy9hkX1jdCLZSGZ+6Rn1W1R885BAMJAkyvHyo9e4/edRvuOzIPrttHODzPJvsCJ+FZyH9goAscEJZREQd6TpT/fUAmSwCAOPeI1gJnonysTC3ORxY4Rv7mPw0I4/MAMwUNfHBmnputSiaPUJoDYVyJFPMbjlH4o4+zzKIia3RgYR++esI/I23ejee++VwMjObXdQfGnIQZt3JkoRdi87hMxPYyBuKiU8rs2DWpXVXAVrbec3mpzQAwrEAY7xLLqsgOHwbi4dx2xzVvgqdAYUrKPh3b7vsizrrx/QiQB6smNqwsbUtRP59/sEJo42Nnzkw82z+yhYW+36etPpo/9yNOX58/v1Um9W5oM+JU6bcIJmq3FUliQH8jR1aknjmRJaoojABMG98v0h73c96nLXXxnLsvrzxmIXrwHTmyrQdk0UUxQmeuRwgz89nZrrzOLTXHOTpUc9yjm4F42IAwQh1305P3uMgyZ8xmWDE8iFgI3LEyXz3fjQvyS2Ievhfib79yO35491FcefcMtra1jx6BqPD6FCDYteFxOOmI9LlbGLDMg248jn1vfgtSX95/qS/Hl2Zo72MrHuU49Xk+5gYto/XwyjPN6zvOORdHPvgh83fn2usAAO0F2bapxkEAAKMR5r/9bWRTec8nIA9gJnv3lj5fjgcnis9MJ6mZCzgROY8nY/2f6+5UHhxerB/Ieo431CLbAkDm/v5kPbNMR5jZPpoXTNuL0Xzik3N/H+0cqdlSBitUgnBWfy5Zak8iEXKsD4RdYxcjYPL5SIhACh/Ml+d1bl/1MQgld7o+zeCrPmmqM1e5rfYpheDwPTvvYF4IUeXBoU45IykCDcIQHxAC3cJcj6h5JhEMNFJ9oSdKTBjBAV/1+8W5nNxBxXuq72MU8KkHEA++EPjx8XLjmzcTpCzffv0bnh8gUt5drhyZO99LEjun10wYPW9buSDzDMKZSOp1FjvG7D+fq74uy/Gzxc8VhDmW2LVrF2644Qbs2rULjDHccMMNuOGGGzA/bycl27dvx2c/K+nIhBC8/vWvx9/8zd/gs5/9LG655Ra8/OUvR7PZxItf/OKf12H8ykfoiD1GjV9uEMZ3TGfTsH4x4Edyu8YT35sDYfrJnf3fxzz4j+7QU56CtX/3t6B92CnHEr5KMGaq7EwngangRhLBrcprOWDpS457gQVhjiFRuxy/evHMMyTo0EkZPnOdlb4YaYaIfIpQARFJpiv1gLun7wYAeIr9oA3FTaxZj9lVSl5FSTB1pxi4SrZ1AztBGcvy1G4+u1/+nk4aIT8hLS5wPSHQCzKITLYl7vZfcLisCI/14DlJ49in+NDpjkxhrgJS9S/Uvjc/GORAGJ9FSOBjIXVBmPzzR6Z3YtUZsuKpUaBZezUVIWGskoeKwaOT6dTr36+941HvsG2jx9YP9Adh5O+eED8T333+d/HEzU80C42h2B7D489cif86S0qoJqIBnqbwAISsAU8ZzzcGA0c2SZ7bDeGI2cf6o05F18azAAAj6v7bcES2MVbgVO+mm8CpvMcmVP+5/v/7HZPMdmOiuwatBfW7hbFjKA5sIgsE+978FiC1ppsaHNEsskQtcpgXQ3B3Aq+SN4TBGxg1njBxxaJYcAvycJKhEXmA+p1OPA5/RZ5dJhRISZWW96TyYuI0wMGFMsgpHHN7QXjOE6YqelkKqioMg2YLgfL3oSIoJexdxgRRz5PYMGGSe2m3sDhjzBi+C5+C+p55jljF+jPducu0MwPgUblf5h1bdcWFt9UkAFgPSYXsbncJeeDPX9B/I97V7N4A8bBMqvq98v3oRk+dAw1geKIaJAEsGEJFZkAYCoJOr2YhrxN8goOqykUKgk63vH3GublOQagTtsoIuCqxwzX7hIEEPo6cuaHEDqGg+MTVcl0CSJA9B8KkdvsiEyYpdGG63xBFhBIOCON6whAfo+01yFg5scPTrmq7SjY7TJiv3bI/t61gFtAc8lWifHCFOf8AELAYjx9+T/43qD22Vzxqc6kNbuwauxvzW/4Gq7fkq1y/vHtf5fZMgTAaIP+X795ti3IBC8w6CRjfSYx/+wyCDwXVyaLhjhyv/C15uboimDyatuBzORcY7XOPn0Lz3pYjqyrMjh56DAbQ7EIHeIEzJk9+9KN9vx5u3Nj382ONDaPyvGyfkAULScZzIFoxfPX8ZYEC5rL6bXm3a8YbDcZ2R8tgmA6WahCGg6mxNa0Bhs13nN9n9wOEOePIPZX9HuGWCaPHjTwII38rSmZwwk8/he13/Cco4WZu8o1bpZQqVX2+7lcEAE/1V1es/3vAl/1SZM65Gs81KJPZMZWTDEEUg257HFIvD96nfuGZ5TMA8Q0YdNKaIUwpT1QBit5dZf/Ug6vOxt3bnl16HwBmhzaDE2oYwpknwULOK865M87569ejHVsv1txcIE1x+L3vLX29q1gQc5GcYzDVd+/69f6G0xrEAYD7nv0c7P/zP++7/XLc/+gUCjDai4AwgvEc6M0XAUoW7sv7lSwGrCws2LnFkdnFC1tSZ3/RZH/T7oxnpi8AAE/0z5OJ4rpG9F9DablP+3f9sbrnodGT/choL8HXbjlQ2pYLjoDJuXtCgBQeMqc4sT1bnqMIpyhHA8PzNeMsW6MKFwWD79t9cVoNwmw9Kq9pnKbwFQgjqI9184dLBT1EzQEJBBDKY/jOYCPHhAEEhLBgSOVpVhMUtwhFZLo4TDJhQCl8ALNqihBkQCbyc1XNhPHiGGEs+yPPaXLmrNGTnlKpEAxDT5DSk1Spzoy11+qm21BzgkoQqU9MfrzIh1yOByJ+YUCYt771rTjrrLPwF3/xF5ifn8dZZ52Fs846C9dee63Z5s4778TMjC2FetOb3oTXv/71eM1rXoNzzz0Xe/fuxTe+8Q0MDg5W/cRyPAQRNRwQpvXLTWNrDdqFuF5EVEWgKpTTDZfa6nQgt+AsxpUn/zxWdD9b+LBMGAB5JoyWeJiYwMTb3451730v2My0+e4QYjMwLYMwy9Ev1quF/mw3ww/vllVBTZVcJoQYMKSqmsrjspKee/lK3ZTbie4CpgEALLP3LyN2Ij7gj6hXauaTyMmrrkASyMBVJQsRApdv+2Tut1odII2B83+kqhQPlitw3XCTgl7WhRfatvsexe2rjsPksO5MnKpBPbl3+pn9Xi+XePN4iPXksEmAEsFKIAwAUE8BRr3p/PsVMl8AECqA2jJhFAiziM/WioZN3FexXfpF6NWDyVGggTlhfkMnFHS1KQDMJJOYi6aQ0gQcAWb3HkAkBOJUVmwGkQc/8NBQfXqkKnE1EybZscNUrwNAPDyOaPt2k5zedqCRa2t82XNMYt5LlCAyzyqZMMPiaaaafXQqf89wIWAuNAG6t94KkaZm31yBI7GfB2EyvwHuLBB0FRUHhxcOGeNKAr+UTEt27bRsD8Kwe7Jjbr80aIEUpTFT+TuaCcMje2OmrOJaC2GZMGBWjqzmvuhliQHKgjBGOCF1vAmPSqyMdNpq+vJQtnPhre+yIMx8/vzzTtcsvLIgAPV8k0zjLH9P6/OkrxUDwf4VMtE714hyC6z7G9MLB40fzHBoq89X1VSGz09Zo/Lvnb7IIl6ZTva8EN6Auu97nX5fAVHXRIOHnqhnwuhEJ+Es1xe129WJl0YirxURwgAqAJBUgDYZF0Zmzo+0PKBcaLPK9ig2Is9AfB+eH5akANMgwNGFxEnKcni+7YN/Smw7iiBM8e/Yr+/TdMVqTo4MwAtufAu6M+XrylO9OJfHdemQ1BwfyEIjy+ns3DKEVBtI2MzhBiGLsK1xBcbXWoBBM2Eu3DpuwJLaIAR+RQ5141kvxxs65fYzKqXcYjV237hnBiSHwihWniOP9+2zKJoXXAAAeNyNAnuD6vFnpKOYMEVT2QJw+cidl5nfjPcerj20YZJPSg+vLGvaOz/S57MHNkSqmTD22riATLJrd/Erhe8vXrVttu1TUa1DJ09ddmk/NoxOPAk1nqIP8NHt9EzfP9dQ/XRcz+TRYC8PJkGHfwIASOrYdipYxzKH+CLV9XVRzYRJMDYvxxmP9cBDaphrxVi/7wqsPXAVvFUnI1TFIy09z1L3L6HjEADmBjfJQhfBMdW0idOiHBk3IExqxiVBMvhhDHrPt5HR/H1wR5gfIwQJMOd0ixdtG0fHX1CfUWRHytX5s0NbK48PkIUX7eZqpL7qaxQI06sB7nVMLfTAnKR0zxn7Kn+HUDAlW7QQKrNv6oOTauCoLrq33YbpT39mydsvx7FFO8mP5Z2k/xypoPCcA0+rIivMLxfbfsdBW3R3084yY6oYLLHP/MjB/n1qL+sZrxMAoLz/uJoV+kSxCAhTZBOKPv2YBmEITxGr+dSmIwmGGuXcHePMMM17IGiyHu5s2k5hbrK8ZgnZNAApH6sZw7NJ9VwyM0WMHCE4Ul/O+3rRcCUIc+Z+6UdDWQoMynPOqY/fu+mzZVa1sEwYEsi52/Fhw4D5OgaZMLL4Yrzcf5Gq9Su3zBOfeMDFbwIBcMa98nif/wOOz9/9edsUxkxdZBA0jPyhXwfCJKrYhjNTxDzY3qeOt/yc6OtdnHsuFgtXXlk+tO6xMeuXoxy/MCDMxz72MQghSv9deumlZhshBF7+8pebvwkheNvb3ob9+/ej2+3i8ssvx6mnlrXil+Ohi9ABYfbfXdZl/mWKM48bM6/ToP5RS1Sl+8J0D9SpeqhKSG4flYZux0olfDhEEYTR+uBE8BwDZvQFz8fQk5+EaKsd5EL42KDm8eT+rX2W41ckdNL8ip8exvfvkjfN3z7bSn6dvXEEAFC19ve4TBDraA7JBVrKQztYigItl1C4pSYDikVDTJWhfJ8bEIaBqYkZiYchCDfVigBw0W0cXxg+hMEapZCtX/pi7m8XhEnCIdAwP0GOAw+ZljbMzb/VETmyh23Bja6s3MLDvRGB0O0VLCdtooMoECYqgDBBWMOEUeNAGg5CgJhkeuUk1gmXzbJ/YX+fLSt+k9aDMDr56S7yDRPGGbOOdI4ARGAmlknrqd2TSAjQyOQ1jwfk8TYKMmz6tCd79hpgAgCCOAahFIkyWWwPPwqAnLsAAFauMmyVtqcWGiyrZMIwMebIseQXGU87fY1z6eW5ZrOzZt9cZbu0HNIZnpQW6jRWoN115Mi4ZsJweNEAop5cgGZ+E0mBeXDwL//KtEeQTBphRvJ5ZF6I2S9/JX+OlHySz1XiJKLgUIlkEpcrphkz981JSXdROTLBOmZBG0SRAQJ9Hpaqk3u7rdQIVd4VQaNlFmVk92R+392OqZrgQQDPo5ZRJkg+YacZE+rcZCDgShIwCUIkFawGHZxQ7NzweMw6kitV8fHb/8u8fvel/2heX+xV+9kd+af3m9fpIrl0YZgwIbiSZwmT/kwYfW4MEwakngmjzhURLMfK6/Sqk5Kx8ukhgsm+S4EOSa/cwTNm5ciCSC62NZBYKUem3iKCAUEAPwgRJPn+f3ieY89Ux1SUCjBQB/BNXCmIRUAYzUQz33AN63UfjHKf2jlalm3mLA/CjKvjpdw3LFDTLodVRnWSvtDPByzGzsDH2k32fabYZK99zHGl35+vUJL2Kxg+eNo/wn/0G8vtp0DgBXjnc+TYnTKeN5HXfZEDwtAWQfvqPLM0m8+DJ4wLrJ+T/Xc4kTd3nvhpdQLIy7qgon7iSQlDk9o+oUpqmfSrqnqQQsuREadwyQVhBM8/g0XZKPfc9ov9b3877rr4kkoJJzd0RfuII0tdB8YCAFV9/oHwW/KNPgnDXrtrxgPNIuknGaafV0Yzk6yqZMO533GkX+6PHBmAGlnFFBPTGoTpAgFF5vU/97w5jnM2yQHRZy8AAQAASURBVP78rkPzSDKOybaWY1yDW09+Ba49R8qdglB0Q9uXGjky44WjjiXLzLgEwhCEMQhQYsJ4XOBRgw6LKrsXN+2V4PzqoQh/+IQTkAaqkIEQKfXntp142LP+UgBA3DmC5kK5sn52aAsyPRf3ZFHJjqNtZx4g/518ps3pJCkzhRYAkAVNsIp5n5HkcYqt2qFTeEHD/l5JABrnntP38+V44GK2IJ+5GBOGFMa3xZgwSaFQZrHte85ct7OIUgEA8CXIp5l9F4BvuggTJkvz/dCqvf1NweOsKLFVf6zz190g28BTw3oX1MN8VYGLyOAz2YfRLMXffeED+P8+yXFwYAcAYGGqnLBvZnI94DlMmJlO9VwyM8AYw8y6xyHzZZ+Q+Q3DondDe2l6PIXGqPXaqwiEWzkym4u6qDVRkiPLfjJgAJKBFcfhTz97M05/29fx7m/cWdlmwGHCaBBmuzSyX+FMI6/63idMTsD1ePGilmmPK0fmgjCpul+oyEBU0UljQUorNtJBQBSY1WpO8EDkEKf+3//72XfyKx6/gKnc5fhFjsiRI0uKutS/ZDE+aqvhOn2etFTpi171+XsMMAFUJyRfd9brABw7iv1wCK8gRzYWSpCqCMLoCB2piND1f7jzngexlcvxix4nry2b+p290SYf147I55KZyad9mDwOo0Ptx54BC5IsQEPNY0LBsS68qfb3tw88Fryzzk58dOW7WvEKkqGpZBbQGAEAzDQsIP2sq2W7fny2YuycsSm3/3DbttzfbqJyYH43ggIAEPnU+L7kuw0FfLjeU4Tm9gcAr1+10iSRq+TIcNHrDWsv7OWB9bhZ7enmjgMLrQmbAFwEhHE9AcgxVhT3lSNT4IObENKeMC4TZqI1AcDqh099+/+iRyhiZVjdUCCM0S7XCQMtpdiIreY6OGgQQkAg6tmF0/O+z0yiIU16Ro5sSreDZ6VqfADo+rcaAMIreKg0As/cj3o6nh2ddOTI5PZaDulEuhVgcwChuOmA7W9tFZWs9vdYzyS9u0fKiz/jOUMYRpoBiKcXJBHaV12F7p3W00RkGRgNEAr5/PLBAAJaUilGyvMLLeH4e0ywxIAwgni494UvwsKPflTYvmukHbwwRKRkQD0eljSiucuaCpSESyMy8gQldkuvZxKAnhfAL7A43MpKwyYycmQUXCXWF0Yu7AvC7Nj0ZNyz7TJce86bkfW5nztzFkQ6cWw7/mDNpbIdvBrI6N12u3k9ikUSkW2ZqO75AdCQfWWULgbCyPvAMmHqk6+6+pIKDgKYhXBdsoOpa0qEACcWVEkqEh8p54axFCpwjSr2DK9oj7bL0gxAz48wsJBnPQgaYmV7Cr1Ey5HxWqnEH55cMMUtzAvjYrGO6wmTZRAAfIyU9lul686ZTSwAQOBrBnJQkndx/ZWoWsjnJKwgZdd8AQyOOvedJ++FErMGwPdPsc/Ilc+0DKiqCLwQd67Lv8eIZMJoyaX5XlYYwFR/puQ4MgoMV/iEHTpya+7v7uEj2Dwn++/4+OMQHW8BpMEj8pxNhbfkvkNrnhs3zh/4JEa93Xjum86u2eLnMGnX7Kk6H8UCqKHPpQ7eW1ql6/T/+yTY0aOY/tR/992up+67gciHp8b6fuwGnfBK1H3WzxMm6XQd+SklP9PHE4YZfzNmvNgWlSNzWWiiRh4L/VlBRQBUfiHBXFPLziQgmaj0hMl9BQJbVtiCoTsPzOH7O3eYvw+tsiABZQku7Nm2dqguEMzPUUXGHCYMQ6iAW80S0eExgRMHvoz1w7fg5Nv+Dzi7HdNduY/VQzGaoWeK5dJgAPvf8ie5IoovX3gCANmvnn/t36LRLcuNzg1sMEwY4tmKpDmdAFaXNlltlU2qJCt7UZkNowsJMiWHm9IEKe0ZiUPmRRBp2jdBPfYb/eXKluOBi06av65VIIAbpMBk6ddvAMDCQv55XUyOLHUKPEZq2P65WIRZ48ZcO++vtxgIk5ba2n97v3hP9zk3k5+R9g4ezyyzm/qY6aSlvk8yYWQftm76ICiA03cIzIfTAID56fIcsaceYl8AodrdbKe6EMKwckSG3tA6cCPhG1UyYbSXJuUp4lj2I3o9UpzvUweE0cUnXhgW5MhkPGlOXp81gxP4xNW7MNvN8MHL75UfVhVaKNBDypF5QMXc8NG3cCNJxmbtOiqIW2bsXjUD00+7njBpotrOGeArz0u1Dvd5iJDFBU+YPBPmI09UeYBGP/ZuTSyB/boc/WMZhFmOhzSCyA4QE1vLydJfpnDlyFi0+AJseFXTSHRxwm01tBOmmrvw2Sef/vA/l8YThlMILvCn5/8ZAICAgwQVVepO1aNwFmp0rLqadzmWAwDWjTRw/hbLQnv2WeuwYcxKqOik+pEFOcFyp5Ieh9Gh9hu+qZZ35cgA4NfG/gIe7ATNfRrHmi20d/yu2TPtqEmPNhwmGV5/zuvV97SGt23FsJqDm+LETdbwGECpX9AVhQCw8b4vmOSGjjjwquqPraits/0fbHgSkrDcl3QTzYSpAGEe91Z4r/kGAKlZnvvtgWrpT+0JA0jpCesJs/REVexXAzw6iiBNv+0jw4RRgELKzGuXen/ZcZcBAKY1CMPWYY4SNDQIo/p88x2igQ/VFkLMQoBAUd8FMHHwKrU5w3N/IMCULvJ8b9YsJk4akXJC2HABVm6rMN9kLVPFmhTOexR4gNMG+D7Y5FEDwjCV7dJJ4DZvgAiZ+JhUi0IhBAjXUloMni+rZPXC4yf/ZCn1OrQ8jCAMkU9BqE5yyPN037OtJrxIUySBajdhCGMPTd5RTW6gWwCeknvvMfdNgMyweQTx0LvhBuz6zVfYfQsBwXumStYLQ8SRZmdEpepkw8ziDHxU9iVxGBo5MlZYGAuHlUM8isHLv55jlLlSV1oiSJ+bjBBwutl83jlqpcFMqIXz0TFb9btz45Owe03185J07XMYeRECdZ2zmmr+xJFTfF5BYqBY3c678pp0vRBoyr5yzXS5ktkNoeYtAdUeL3UV4bbCXINqevHfrSnaybQXDzhmmxa0SSsSNYwL6CSFr2TmDLBWKRME1QYG4gcIGUWYziNI7EKZeRHGu7M4MCPvT0EYaA3rYedqguuOc7TqC5fPSnrpHtvZlmfoNPJjgY6MV4Awmfbxkp+F6tluJOPopjyXDHbvX1+BL0UQBgBSQsyYKNsvj1mzBS9ef3Fl+w6tl8fj0erqco962Hww/56WI9N+XUIAN+51ykfVM6HZGokPjJDy/rPC6Df9X5YlFq5eDX+lPac6Z3/yzjygXARhNv1nXh/9D9PfxSnNb+LFl92H1VtHKo/R3BIV8/oHK/S8mdRIxRWZMNrvyXyeLA4+cTdpSPunFFI1fviUmuvaT47MgjDq2WL17cncCmqhnukakASwni6MZkvyhBFCgCG/TqmTJOsnZVVMAMqdp0amlPIMyHiOfUIK9/CPjydYG45iMA6wThUVzXVTTPJpHGnm/S0A4NRbPwL49r77ypo/zP+8ZpVlmWXqkhShMqnu+fnE8IYjAguE4OjBHZg4dC0EAbqKQvn2Z54CQggacxYEOjJ2Si5hR+gIAKC5cAA+62Ll4Z+U2txprECq5uJ3xXauPWtMwTXT3B7XDqfPaihmGvMqWIKqz9AgTOJJqVTNPtLzk3TfftCWBLrGf+uVuX0MPu5x5f0u1NDXl+NniqLk1my3f7/kFUGPPv0AAHR2yWIgP5XXbzG/J104CywticqT/qCOG/MFJojL7KqKrHCsvI9XphACmcivZWnF3KG4L+KCMKqfOjCbbydjCQIFwmQO4LqgQZgKJoxQ+/SEMHJkMzWeMJkZJxi2Jz1ASaFmNSCMLl6jPEVD9WN6fVRkYRNH6UGvb/f7eXlbPRlcrb57437bJyaMY6adOmxXxxOGa8al9DH98y+WWTONBEjVbx181z+Y98NmK1egfJJSD80xYRLtpZnhu/fIPs9zlBLWzOaLNkWBCXPbRtVWzpdcdLEcD1wsgzDL8ZCGm0Dccma9aeIvQ8SO583TL9hQu93ZT5LSIoOjsal6EDWaW3U+CGmF/MHDLXxHG/3ovgW0fCXbJLihUbpBCDGLukOztvrUu+TCB7mly/GLHs88Yy0oAUKP4imn5SVHmEkElJMhHrcVNF5IEaiEU0IGTFI/hcBu30cAO9HpOrmfwCcQ8JGpDEJLATiuHNkl6y/J/W5V1SNV80Ti9x+mT73lw/YPnsAvJEJaDvCNXCWySrY5i/P1aYp9ax6Z+/6l7Q6GoZLhgpVZa9QD2XgemhdcYHw5dDRb9eDw6Bq5wGZetGQ5Mje5GdQk9HR8+bIv538vrgdvdUJIV4jrRR4lwEBo+6bAC9DAOkw1FQiTrcc8pYgzuVDXTJhBXR2XJ8JAZJkFJpABXoDmWWchVBJHgnjyfChK+kz7qKHVb2mp9rfGMfyKd5SOYajTMFWsh87Iy65uGG3YNoDAHxsDX1iwcmTqtGomzByPAS4XGUydi+5tt1kPFsoRBi1c92u2au3Wuc3Y/xdvy/2u9YTJsJAwMMWy0MkPNzHDOx0rBRh2EQUeQiEXBQGLMZ/kfW4Ovutdpj2gIRZWa93n8vOSMgEPCag2Pw58NFSFfZi1SokxlmgPpAxstew/wkZoEvx0Lp/gFxkzpt6eul5UcLO4y8ncqEWQTrqloEiZrdbv7a7waWA9dAMLEANANx5FUjPsp478UOiF8DUI0672tUgdqZrzX/Dx3Gd3XZjvD4QCaRIvQKxARi3vVBVCCAPCRIOyLySE1CZfrQ65BmHk/dfrVScLNDAowJH6dvu0IvGRZZbx4UcNHPrNi832VSAMUQk+3e+NfUmyqyKH8Sc9CAQorBxZHRMGAK4+0fZx4wVly4FIs+TK4fo1FGOmgonE9EJc9fmh40s4yAn2TTsVp5ybfsnTIHsFCNMjwNCo83ypQUoXNrzr4neZj4hzENoLODj9BZXt76QdRAXMjFN57442beL75n0OOKKr93VC1QNGK6SHnv+jNyMRjmTZxFrzOhxogoTud2RDg3QewmETejzB+G/YtjfPOy/3G/eIdfjkU28GHv+2yuNz9y2bfv+krI41dMIFNT6KvdvvyG9fSMIsXHHFor+R7reyoIff/e6+22qpQd8jZsytA2MBC8L0fDX/6JPLzDr2GuvkGennd6D6fEYycLo4CFMF6NRKkjnzlHbhlqzs90QKAg3CpCBMICX1PluTA8AJLTkurRmWIMPh+R4ECL5wyvtL26+YvBXwbJs89V1B7KwAkPeLKyG6alTO347L8h3VC77P0OEEF171VQDAuXcJzHRlfzGintd1K0fM9jed/pocq2TnKslOGZzbBQBYeaTMLG83ViJTrPSe38ZKNXZoaSqrpCaw4d8+jPFXvQq7TpXShYykiImcv2QVxTeGCRMPqv3Lc53q6nrVjyT33gPSlHOVoac/PbcPUjHPmP/+90vvLcfPHqzQXxblyYpRssJYBAPhysQ9SOUcs9ofzmmPM59bjGUDlOXR+sV8Oz+W00U8XgokoZznZDESxoECqOOJfiCMlfTSRUV6Tp+bPwBI0w58Jp8b3ymm6wbKz2mhAihR+/QJRaD6oHZSDQSYvlYwiInzIBwmDCpBGNufNuKGee+6VSeUxhzfYQ3r5/qAl1UzYNWccF9hDXDb/moZOL2W4xTwqYcv3CQLln66Nr+dBlbmv/Ut814UDeDoFHDb9pchCQYwNifPQeYUeGWZZcLsni231+dhfr6hmTDqtto3DvAogOj1kO7aVXkM/WIxCdLl6B/LIMxyPOTx3Leci/OevgVnPKYemPhliCDy8MzXnYknv/Z0/Pqjt/TZTiVjeplZ9AtSPbD7NQPs7HD/hOTDITxnBcVSDp7YgY/GNVXqKhEwRCxVMr9oXo7lKMdLH7EJt/3lk3Hz25+IJ5y8OvfZRcdJ4/XUTJ7thJFyW0HjB9Q+m6KZq2B9+vo12ObJBdfA/B7scH7iHCV9pp/hQJkVcvU7nrByNRqU1swKN6j2Thoc73usq47ciEuueD0e+73XIqV+bgIMAENxYCp83apBLX2YY58kC9i882u57ycAIpUspzXSgQDMM5wSOSkbm7wNzWY1Ewaw/Z4EYbQfQX8Q5tQVFlzYPdffVHjD0AYMhvW/74aWI/vOHTKZrKstB+OgJJHWwV5MK0+Y6WytBGHSgieMAcUL/XiWmUoyKI+slW/4w5wEnCAemNL5TZKuSQj4ke33gpWFGTyAJLBMGL+Vp5aPtcLc/cvn58HbHQM46op8XYk/zwJAASCxkNe1d/vtFoQhHFHQwvypJ5t9Ds7uwPSnPmUm/K1HPtKRI+O45ISVGGnJvzvxilL7RbuNTMuP+AniwIPH5YJ0/WSMJ/3Pk9AteOEYM3FCAC/LvedGL2MIifVc83yKkTHFDMiGkBSMXllqK+OoSuLEkWXCiII5OtLEYcIESF7+avk+nwYg/d5Mm82iTLHsQPHk4+3i6dB8xcIv6+HmrXDuHSlbQ2vWz6lTwUcJNf1NBgDtydL2u4fl9vMxsGXTRfjIi+sX5kJVKiY0wNCclZFxtazd4MIyi4JR+TwS0OqKcADM8YQBLFOl26mufvU7Mtnmc47Eh/HtYZUgTOKAMBEwscrIkYlKyRAXhPFB2/I6+swmIJgXYzUmjQiIIByecw8+bvUFuT1efqp9Dk/fIfD4dc8yfxv/qYqmiCRxTKcLG9x7Yml7ZjxhtN+TvWdXszwI5nrCeAU5Mu4wPt89Nor12yJsPvhZnHHTv4CoMa6lgOpm0MSwkv/57CMpZhvA+CNGkKi+Jxju72XkRuYRBFT2v3/0BClf9IO77b2r+xnes0yYoYqq90RkuDG50Z4X5VN21/A6+JRg1Zv+2NlaX2+ekwamLEHzwktr2yoIWdSn4KFkwJjItCdMNXiXHcqDp7wgBZMsISlT3Heyc2d9c9ScK/CIAfzrwFghBLyCHJknfMPaKEZy2LJDNxyW98TgoXrfOG6SUQxcFYhlfaTRqhKzvEZmiDiAxweenk+zVHphiRRxqpKGKsE2Ol8v8SgI4KmxQAOgr/vkDQCAL+6/B7eu+JzZ9tLLfx8A4DnXaSDOz4sMQTLLTOKVkAzjQ3JeMybKgNDt3D5rQx2gp/wghhTL+dfO2oDtd/xfsw3r2n6E+/L3Q+XnFWQLWH3wxwCAk+74TwBALx4zUmIDPjP71V59lmUsMPDoR2PVH70BXQXIMpogCKy0WDGyo7If4ZEq+lCu15oJo4tEdr/6tyEUM4FE9fKfOjRrZjke2GCFZ36mZi6gg+rCjMAsfPpun0Fe2yCTc4k6hpvZPldUszgIE7bt8+MnZek9N/ZM5plsunCoti2Fc9Ouy6VA9r8zfn57jy4grTle7kh6UeMJU+2rwlgPgSo4dOv+NKMvqSiiaar5sc+ZYcL0auR4LROGwxs/wTBhmBeXmDBCiFzbW025Jq+XI7NKD1DzpEfGE5UgjL6VOgVWZCfNqhPq2n+KAkT4mEnkPbnxOAtsexyYVexqV8pyZmoCn/333Tgw8Qj84KJ34rTZ9+P4w+cUPGH02jxD5oD/W0LpjVeWI9PrD2L+5Wq9OPed7+LQe95bkmFMD9YXWR3+p3+q/Ww5Fo9lEGY5HvJYvXkI5z99C7w+ZvW/LLHhpDFsO62ccHJDS7SlPWb1P2tAmDqZC//+6Dn+HEIbMLKMgfW0XAav16NUg2vgJFTWj2yq3nY5lsOJOPAceRcbWiqqqgLTZ46hX0CN9Mo1X90DA9YIAU4IzqafxAk//STW3/eBXIJF75+qiY9/s5pUa3kYYn9389BmAMC+obtLbdGYJW0MlD7b9J//gdGXvQwNVZGrfRBS6pUYPkONwJ2COZ/opLFzjk56BtYcyBsbpyKGL6xEUK1hqUre7fPehVNu+RDOuOkDaDTrmTC63/vPx7ZM5ehinjBuhfkdk3f02VLG+x/7fqxursZ7L31v3+0GI3lMuqp0RlXauX4wbszGMuHTE4NY4ENYPS/7JC1H1iyAMMYaJk0tE0Zl0L2BAYSPsOw+Tn1wBcKwNLHVaLFNAgyMlvtLQlqm+isIo8JnxFZDEQreboO327bqVZ13LUe2kFFT2d8gitY/NGQStYxwRNEg4qCB0UnpJzI+eZvcl6oyjU8/LceEGYoDhLE85unRE0p5Zu4kmanPEAcUXqK122N4TODOKUvlJyA2cUx9EGrlyIqRZBwh6cFTC1rPJ2g17GKVTuUXW2xaLjqIyOCpNjUaEUK1UOqFQ7j1qGWviNSaQlMawB8ZVd+Xi+/EMXI1Rp1qQZjAw8jQuDEH3bU/L+knG5QAIp9QojwDrUkCpgUta19l2VIA6JQr125WPidXnCrZp/FofZJDSxb0vABT51tGX1ZnqMq5eb5DDcIQil6xhFOFlgLRrDo/lYmRbkUlJQAMTMprQoRmwqhEWkX/ztKuaUsQNhEEsQF5RGWFuvJR4Swvw+gWFnoRXuJ9G1Rty5GXIwsaeQaeKPRxL9r+MvNa+59UXtWkh25DAvJj3nTuIzq7urR50RNmw4gdRzgKuvqOJ4zvq+SCr591e12vacQggmPjoa9hfPI2U73q9tufe9bn8KrTXoWpQYJXvc7Dis23YUFVl7Yq/BmAvMa5jk5o5Xc1qJ06CS/tg+MyYRoVTBh5vE4BkEqIHW2OYLI7ie/Sn1Z8o5Co4ilI3H+OXfLZKYVmwmLRpOADFUf/7SMAgPa11y5p+7lvfDP3d7Bx8UK5Ivjaz0ejWo6s+rxl3IIwWx3plzq/ht7732te6/kQr1kvyf0oOTJi5ciyPhXrVYALq0vWOooFs838817lhSWQIcx00lCez07gyqsVtidWFeH09SPOZgQxF/j+8d/FY773e3js914Lqp5/z0n3tJSMpN5tcEAmhkXGcnJkNJDjDaOF6nQh8LfjVvK3HVnJXu0PRVevxJoD1pftw2+6xlyPOFMgjCPreMrtH8Njvvd7+Nb2a+Cn8xDEw8yQLF589JZnm7lYUYrKrfJOVBU9pynCVfLezbxGafsdz32uPC6VTO8qpvd8KMfGXmT7bD3e0Yo1Kh3Iz8353Fxpm+X42aMIwiwmR6bZsTTUsrL9999N5PMQaDmyTrvf5jn/uMX2DQCNhWm7PfoXkf6/W/5P7u/FPGH0PIpweZ+mXv3+My7gqaLARIEYJJiqBcItmyTvCQMACwW5V5Z14TO573Puusq8r9llCwtlIPes7HoAUpNBe8J0szoQRo8TGaKohUT12cyLMPnxvDwoc/wiKWdoDSoQxgsRsMx4k+lwQRjNhHl0c0MOhDGSkOqfyW7+nLUT5kh7O6xX4wFp/ZABYKsz//UZ8LTPPk0fKACgE4/hyu+eVDoPj7v715E6csOJ4wnDnHEnJPJejrJCv6VBIXdYUnO9w+95D45+6EOY/dKXcl8Rab1P5eyXvlz72XIsHr/8WfDlWI6HeRjJox7DH53zRwCARlhdzVAnR9ZwZEoerkEIMVVeWcrBjPa8qE/sqtj8FUtXr6vqW47lWEroirpuxcSTCitH5gceMjXZbs8k4ERPblUiQfSwft/3QdkMtg5vNfsYaSpz9o4CLrqZ1FuF9gKxEzv9PLv63zqEmiXRsFyB1zzvPEz82Z+ideEjcu9LJkx+WHeBBMO04xxajsxzkmwY34aN5+UrsVIRI+AahLHGhcUI160HAKye7GD1kZtAIBA26xNXGuCabdm+azE5smONc1afg28971t43KayfrcbG8dlG/Ri5NodslJyoMZ0M/MSLATTAIAtt70Ha+ak7m5zOJ80LKZthMuE8ZxJuMNcmRo5HlyBz6I3Z0BB35FFG1pRPq+CekiVfEfUKN8zq4bziZe5b3zDJlzUwkNXJ8/yAL7ZUiVxhkccOTKGIGihETTRbO9V78ljN7rojBmQhxOOodjHwKi9z4ta7SLLDMhAAo7I9+Dxntk2yPIJFzo4aNrT9EIg1HJk5bGkl3FExHrCUI+iEduq1ZGj+UV92pOLcCIYWFNKt0RRgCBTi3S/gY/c8FHb9rTnJLED+Oo86oVx5hi5GqNOdc57wkPr1JOMaf3N91T4/WQ9EE5ycmSZH+sC3lKcFeeT8oH6zZQQVJmH6v1oneiwGgYAkJcj4+stsyFZqE5cyAWx3HHk+Ll1a3TSi54wnvICqqqkBGBk4IjgYMRJwFYkTZOuTQQEcQOBoNaUtSKJLgpyZDqmR0+wx+eFECAGdBckL0fmSvB+am++Mv+zFxKMNe011QC+vc+dxXzSM/d2QDJccsXrcvtamMmPIVpvXYMwg2ETBwd2AJBGuO/8qgNiO54wlgmjnl0UwDXBDYbgU2GS6TpWNFbgladJ/wRBCBJCsKDOQSusrhRPKqpfO5GUfwSAszaOAACmus41EnlPmNQHgkICqtUR+O+/zfDEj1pZreQH8vXq9hRe+pWX4o8vt0wYQfT1BrLxz5n3Kc9Aovrk1kDaKSWlSvFzWG2zGZmsSfeUfUJ0uDrwh9/zntxn3sjIor8x9808cFPp76hCy5EFHrE+bDWMuIwJeOr+f9H3bN+S1WwvjlqWlJaNFDXrJcCCOZwwcKrXJX08ZPT24MhIknuvGO48prjHKtBJiDSX8JTfq08E+wzwIwlkvOxCW5Q2JxrQDoBFHxnPMexuNpv4nz370UgUM2m/BmHs/ITQFPAi4BGvRUrzzyeBABVAOCjb+u9PoIDwsW1ly/R3dNMGfOpiisE5y4xqN+S4ZEGYudJ+v3I+QbMjx0Cu5gPbN24xfWORBeEepU5ICpogHpTziyTKFwK5VfP6nDN1/bUsWY49o7avYsKse09efs811V6OBy40CNNQ86rFmTDqXzUPr6lpNdHrKR9QVfDBO/29fbjTT9BF9g0AWw5YJma2SP4iLbARNWhSu70GqBRrHDWSpYDsf33l29JTMmFRb7zen89lwmg5MtU/fPiKe/P7zrpm34OdafN+SxXn3LC3LDk4qdaqBEComTBZtRyZlt0SgqEZDQC6D65iujlzTiIYRsYlWNqNRhHwrOwJIxwmjPbEG9uMQ8Pl++wqJoGRKTUf1R6skglbMchrEIYCnRos41G3idL2uzY8sXpjAP96rR2n21o6W2TIciCMvJdD1iwwYfJyZADAC8WqvbvvqTwGHaMvfpF5vfadf1fbzuVYPJZBmOVYjp9zGCZMl+HitbKy1K9JdHo1A2yIXwxgQic8WMohetZ3oWg2Xow133fMzJZBmOX4GeLECbkAZJ6eEdl7z+N24hmENLfCOxpIKaw1k3rSq+m8wL0zdkI6MRSrfWnmAQHu+rqZvGaFJOhfX/TXORBmn85T6oVEHxmE5jnn5v7OqpgwsV9ehXTnbUJ9IO/NNboxvwDJRIwBrakveO2z6q+SBscr5+zENWjUU+Mj5ZmlTe2BxeXIHqzQicTJhQRfummf0fddSOoTazONsr/G6IRMMjY1YGIoMErLt9c1iaEhh60QOEDU4ZVnoduVyQkyv9+Rx8ufGwLbNqqkhxJVad5qVuig6wSouu7tH//YeEykatWqF7l3Z46Pkj6ELDXVZYIwEM9HM2zirrXyercbkvGpfQUE42b/gjAct3oAQ04+xPi/QCWd09QyYQLJyqFKEi3zGwgLl2L4GU8393AMAd+X52zf2otKx55kHCEsCOP5FJHz+/v8/M5TxeYhgiFQyWgv8OE7C0SPOWN00jPn1fdD+Ar0GF5Q1YlOgtbKkcnvt0mMwZO2G7AhrAInWAKQPPC20FoDn6HSX6Kh+rSLPXk/jKup/kHfAyrMrX31k5kaWmebefZGeuCAec2VHFnP89GMfPTUcSTtcqUjACS9FHqpEa6wTIikRr5JV6gTzrBnHPBUUjKt2T5VLDYiOI474CRgK5K1qWP66scNREHLSMxVVbQSYRfy8H3Q334pAGDcYUExL0KXh6Ca6UB4jgnjvj45SfGsbVZ+7I71BGNNex+2ip4wzrUl3Z55nigR5rzoaM8UVvhcy7rJf+PBdcbgPAQHd+5lV44s1GCTSpAIUgBhOANX4JQH4D0vOBPFCB0wZKfvWyaMXw3CDFcwZFwmzGnrRuRPV/iqCFX9nvqAXwBhLrxDbrN651Gw6WkAQHbF5QCALTP7sGdeghNvfGVxTinwhB3fsIdMvJJk7ravfdW8jrNenllUEQQ/n7Ft0aiREZSfLcbuAQ6/Ly9Hktx3X+22msnke9RIgFbKcwFIOTcMXI/1TKKszq+Bh7J/JIKZhGEVK9Jsz7RhcgauHn7W53g1+MNoCqZYl3VyZLkoXPaqqnMC68ViQGHuPnf534lSwFshJQg18wQAJsUQgoom/WQrge+cizhu4IQ0RcMkk1UjWWbNuJVnHbwAnYoioS0HBKiS/FqIASF8UGdu6BEPPzyZ4OyfvNe8l4ZyrtdIy0wYAPj8BQTMI9g5lpea3bJxpWXCmAR8fm4FAJkCZAVJMKLmYguNfEGCcJLcuuBKezKmrs9EIaqYMAOPfnTubzZdwWL9FYq90x3cuHv6Ad+vBmEGVQFdZxHpR82E8VVuhS4mR+ZpOTLtCdO/LxcOA46Kar8oNzZM2s95H2AYAE5oKPaDGrd93j9Ny7guHND3dR/gmQv4CtSZD6cBAA0W1zJhFtbLteGR0awkR9Yo+BB30nkDwug5FQCcsEf2Y15WBpP+fUSO+18aaBk5ska3Wmaam3krw7qxURAFDPcq5g5cWCYMERzNAdnfcC9EwFPMdfPXl2r2pmB2fdsYReqV74M9XJ4TrrZ79PFy3dNJGAjRcya7vTDjDEBVDuDT2cWVx+jGwVVnmdcbd38KI9N3mb9vcCTrOj3NhGLInD6eq7nr2XufkB8+TBGYfSvcly/80sUb5hgK7NaJt74Vw899DpoXXICBRz1q0WNZjvpYBmGWYzl+zuHKkekq0LqC8DoQxl+EsvpwCb3AyFKOTGkE91EMKAVfAmCzHMvRL5qhjzigZuGt4zdmZvGnhyZt0jv0cpWOHV+CDE2V6zqqbtyscP8SQvCUUyeQm/ksHDFMGF4ARJ513LPw4af/hfm7p9bUxhOmjweSvzIvdcioZypzdLhMmEiZCYr2jJmkJmEZ8G1lVgOWiRicqwkt6hc/ui3n3WWPz5XQKkasQZjM6pMvJkfmxsXrF5/ILjWazjn4vU/8BJ+/QcozPemUidK2F47/GgC7cHdjdKKp9pfvj/UZSXodk0hlDquB+PYajU79FDd96oMAAG/hkMPMyp+biNhKWS2joGNwuCwDNzYmk4gu80gvqPb4Ug5PM2E6GbcSe7rxWWZAD40QNsMW5gflovHIyjPl5nrCzpgjR8awohVhNBpGO5CJl1yiQyUC9XteKP1pqJJMSIMW3vxpBsGd+49YOTKfcGRdyUbTvjJuSCaMw0DzCRrhAHYP36F+Pn8ts0Qn5lMEDquBigxELUYXHANVkaYQ2vPJD8Gv+J58rQAB1yTeyJGpxTihHmgQotuQi7toruwBxdMOCPIJ7LnBTTiy+hlgvLxQZGrBGqpF38hGCUzNUyoBnUKcOivP+1Ylw/KNAig1f7llEmgmTEoDNAIPiWIrpAVDWR1Z1wKPk9Qu7no1iRS9aKWCYa4BI9NWVwGfaY+PtSpha5gw5e17XfucBEGEMGzmQJgyoKXlyDIQP0B04fkAgMaclXdiNMLI0KDJtXLCcvM06i6zfu86hF6IDz6F4utnE9ywjSBynoOijKEbdHbeSmyoMeS0mz9oPy8A2MJIbChPmHAYmUpcvNb7PP7jyAuARPUhWWKkgjzFvNRMGMLzfQtT0ngAEFCCp562BsVwvQufu34NFmh/Jsxlx19Weq8TWfnJ0Kd45LZxp/+xz5FhwnhlJsxhpxvs3nKLfKESMjdNHGc+27WK4Pgbf4Jdp9j20Xvs/cm8sFQJH27ejPHfeiVmt52E61Ztd5LDiwV5yOTIlhJVIK75rB9AUxP9NOIzNTZ4lFg5slpvKIGGShhRnhnvprp+gD/j2arRHAdG5LVL/Pq5kwZ7OWFgyhOG9QFVtLxhRlPjIVPHhHGTVrtWALNO/r7KC0sgQ6bBD81g49X9KQC0elKCU8cblGdSB6FhwrhBeX7t6EdyLkAcxp1gDLNf+7oZsyOSAH4ERIM4FBT7aoFn35yafoBRAMLDc89Zb3+TUKSe9PTSCcQkkM9eM5UPZpEJs0lNO2e9H+feH1rRMFKN+jkz/a17/6bqnNEEI2tGAAD7116EXmgTtbq/AACh2LBMzeW0J8ztG8vz1qV4wvD5X205smf98w/xrH/5Ie46+MCeBw3C6CKFXsb79lsGhIk1CLPI/jUIo5gwSR9ZQgAQjiymD2V43ydmWm4/RPu2fZ0vmW0WyOif29FsXaFAGNFn+9QBYbq+HNd94dUzYTSaQKxJPVNjbLHfnuvOImAahHHmmEICW1HWX60lVuck4dV0EdvXMnhBE3FPgqsHV59f2jbjwsxniOBoNkfMZ1tnD5XGampMsZxjigax5UB5TG9SPW5QnLRmCOMtee8sJFl14k6DHgRmHf2t6PGAABgNcde2Z+PK007IfaUbjSALJGD9sndciOPuuQJn3/BeRF3J9hxrW0/QubZmwrAcE2aYVvuhWTnkyo9lk1XBig7usNxHnielHNf+9V9j08c/tuzR/DPGMgizHMvxc45QVXekPWYGGupXP5ruAPWWl9sONxC/CI8ysbqiGTeGqseiQFQlzbQcy3GsMdwIIMyES96A25IUniBgnmbCeBhfV04a6YVrBsuEKUYceBDGOJRKHw5tUl6xSF533Ml46lN8XHDNXyJU875AA7J97nlS+GzbzD4EhWTcUCMwRJh4VlWTLkyZhF4HhQr2aAhPPPIn8JVJJeMxIPLJ96qo0oIPaxh9ABArk/ZmYkEYcgxMmPMnypPv+xsrB/Pn8fT1w3j8Savw/HPLmvhPWvf80nvXbPgy7jqxbfpyXbGnAbcFtYBJem2bSHVWhsSjWHFEShYQcFzwTVnpxAUxzCy/AGiF1JFWSudznw1VMJA0NpEEjjeErhZT/+p2z+U0t9V9nGVmYSMUgBkHLRwY2pH/Ie3VwBiEuucHyAIoJQijhpE2cUEYLRGi3+t4CeKAortSeZkELRy/HyBfudz5Tmbkg3xw8KxcESeMxAFD6MgAUp8iCFomKS0K7JC0q03NMyOJpEMzVlJHGklkPQsI+QE8BerphXR+WyWroO8Dz5Ookzl/pcNAlrUBUl7EHlz7ZGSTu3HDUYL/c+VOU4WaqePRcofRSgmUHfB9TO7/SWk/m+6Ux3T8HfaeutvBH9msBU+EYcIECH2Knjo/WUFGQ4cLznzono+aCs+kbuHvyJHNN+ycoS75qvvvdlPgMxcRw4RhaTlRkzoyI0EUIfQbRgaOgCAtSp4JXU3JQMIAkQJOR+ecamovQtdrGKYDB8vJxp474bAVVxyHgAb4zpkUH32SB0FIjjWimWhOz2Be0TSzwJ3aYuXRm83ntx+5Pd92zYRR20ZB0wDHHQ+4OYiBvdfJTdPE3I8NxSgI1q8DAJxy2ycRd4/i+Lv+G4QLZOmCSfwEwzYZ4EaxUGavGgfqmDCRF+H9BQPz1MvvZ7gR5EYfrh4ULaeVegSB34C3YgWmh7fh2rP+CA1mb+LO1crrTCVKv3/SutzvtUmCXtOCYKMbHB8SL6pkpK564xux823vQ+r5i0rkLFY8lB44gHue+jQceu97+253LOGpwgidONGx6o/faP/o4+GiTXyPJXp31Hu1ZerZDjxiAP+6KuyUc+OtQnlm7t06JkzmSF/etFkl9PokiAwThjBw7S/QJ5mqfzdzmDB1IIx7TjtxwROm8jsZhPZ+8+X2Q2m9HNk5d4tcv/G8c9fjKadO4Ncv3g6/Ir97xg6RY8KEGmh1iDAiScAmJw0I4yGV49KZL0bXK9zbAljYmJl+QIIwfq7gJ+UpUjX9CxTYkoSD4MRHnMl+IEzzTJgwE5jIMty+dkfufT/wHE+YzLRZt8X+qJZkShA7Hlg3nfbb5vWeP7bygwNPfYZqf4bfn5w2jPSZISuzqWMpxX9iEUbEL3MIIXBkXp4/zSR/oIKpcdwtbqrrNwArR6bn44tlRywII+cHh7rz/TbPgTCRWJyZ00zss0rg9QVtDs5I+T49d6Sgfe8rzXgwjNU+7D/GrCcM82T/QgWtB8INpYMZKd5ULSSu2TGZ27bba4MqAIg7TfCY/J5+5utCy5FNtavPvR2LGEA9RAvba/fFucDRQdnO6REOv2nXmJz6CKbyHqxUy23nQJihnCeM7mjCrpKsIxQZ486aKXNma9aDU3vCMAooYjyeeHwLxBM4uOoc7N7wOHTHlbSs+s7kqPWCGRyza7lWWzLSN6ZO/6T2T7iUI+sMyHXQRna1s4k9riMf+ACAfLHkwvo8870oQ3r4fe8zr/eevArL8cDFL0LmdjmW45c6NBNm+mDbTOq9GhBm65D1nrh3je3y/V+QR9lNqDAl9UOPoeleq/9AvhzLsZQYigM0emOl94WwcmRh5OHMJ5QXY4CctAluF6BP3fLU3OehR2FWhwQA9eB1VZVQDZAxsWUErfZBI7sUqH+9fiBMBYLpFR6oKPAwOWRnxd07f4p0YdoaiQeFB/C834KHDLGquskQQXAtr1W/eGiedVbpPb8PqGKYMKnDhDkGlhs9FgrdEkKyl2S85wVn4iO/cR6OWzVQ2m5VS05yv3n8xyCCI7h2wxdw/fpvoDthr5Ou2kzUavColgtL2oYVkDtU6sFT8kCMRuYe6PlDpvLM33JBrh2h78iZFZkwFV48p62RixYPA4a1oZkwm71HynarRIdrgG0q7RKbqNXaTVE4iF0jtwEABuakjIAB47hlwiwodlkcN0yiIwfCqIrrzJfvJX4Pke9BqGNMFXDkfdcafvIkNTTK+ZGT0Ap3lI5ZJ8OSjCNy0A3PIyBhC0KBMFTwXPWjMbsUKYKCfISvrqVInAuY9AwgFPkB4hNkZZtlwjiLdKMRLc9N4PuAF2Bo6rOqyeUK6CztAqR67Nv5T3+P7Lom/ufzd+OlH7kaGeNgqljDUyBY7NuF3B/d8D/4yTd2VRrdNw9IcOEPJqdx49bqZ5E7njAeJUgNE6YahNGMVwDo+dwwM5IaT5hwZhqABD46IYy0UFqTKEhVJrFHGRZigl4sF5RndL2SWXrSc4DLKELoN0F5Yvo1F4QRQhjwWXrChIhUn+9neRBmcphbEIbwXNX5rx33a/jri/4aX7pMmp0W+y2XNaKTTEbF0N1QcANq+i17PedC2U+/9Qdvy5+YAhOGH5kx9/v+6Rfi2t3/B3ft26HOS9s8S41ImRSvlpWmA+0jeORVb8WGvZej1QWy2b1GbyMcWdy4HQA6Wo4sqJ+/ff+0/Hl5zgl54GAoDkxfBADd6yVoLZIUAkA3XgUyfSq+eerbcf1Zb8Ds8FZsnX2B2X76Yx/PFwoUrsNX7v2KOfFEALztFDll7dpK+KGGvCaz3UUAC7fDL4CDPElw96WPQXLvvTj6wQ/1388xRHySTOY0zjo79/7Yb/yG8+N9gIejk7Wf1YXruVGMVCWDfEoNE6auCjtNuSMpkyFKNROmhkFnEpUMqaeqwkm9HKpOnArCjSdILagCIEu0rEyyKHNG32fTCjd3yc9Jr1zpnSIxyVNfSYmGWX9z8GGH3bFmuIF/fek5eP4jTjArwR8fn++/fdcvIJD38syILhQiVkaUKHlHIpOdGFoLVvAZJBAQGTHTQUalHNmQA8IspAum2jpURSJpMIB2Uybx/Kxdmrf4IcdH9h/CjgmC+wY/DwDIFINVP2czBTky9wqQVCWuaYrGoE18zw1uMq87V9nkpPbbYyRFAIFzF+TeRrPNWGqs/+f32z8eRgy3hzqYAxSwBxiMMkwYh7FeBxwAgKd+PmooEBcEWZ9n2xQ6KaCBs0XWIc7xURC0K+ZSbuRY34T2BW32ze6QbXEkvTirP5+ZlsWG7vO82vOfcm6YMCewaQBAIEitJKQlynEMP1OO9dyLTCHizqP2+U179hzMTVhGnKfOacCjWgAdAKgnO8uFXnW/x804xQBC0Fv5ldp9uZ4wM2MCXsspPKM+pgb/AR+5+SP2t82BOu2Lhw2r2o1zD8oiA04InnDy6rxMYsX6VTieMG21ZhgbbGFwXdesGQDgnN1PNvPQw0pV4JwLaQ781fPOITjrUnXtqGAQIGjMy4IpuiDMONWdtXOT7JBVudAxvya/XiSF4snO9deb17/Ze+DmJ8uxDMIsx3L83COI7eRY63rXgTAe9XBheGHp/dn1Iw9K2x7I6Nxwg6W0ZhxcDx6LueY5UaXLuxzLcawx3AhARH6iwQEIbpkHYeihMRDmnk8ZAuuPILcAfc2Zr8lt8fzz1pvFoQABiGcYCJv3Hq1sE1ESEStnAY8JY5btRfWJBG+8LF1U9IQ5YdWA0agVBEh27UQ6ucuCMEWPpbNeioX9kanE2ksHbGlTn2c13LSp9N6xgDAcvBJUqos6acb7G67BdNFs2o2mqq5N/R6OnPhmXLv+2wCAlsMw0RXtOkVA1AVIHTky37fHOvLc5zom9BGoNgF2QMGitFuEafNaV6npGK4AYR5x4mbZFnhGsku3JfKlFFYzsOc07ijJsbvl/cq7vZIcWSMcNFXEXAEolgnDTeX+0UAeWxBFhn3CnEpeUZAjE6H0hGEqAaNBGNcgkjtSOSIYxHEDXywds96+l3H4CjgS4OBTk0DYglAa8CFEbmGczupqtwxeIQHlKeYEHBBGZIn1pwkirHrN7wIAKNeeMC4TJu9X4PmSCSPUsZIaJozwpITLitH8BpdPvwQTjOKZ7RDz3Qyz3QxMjbM68RZ7sWqPh3Ov/gNc+b9346N/9H105qulH07t9XISHq48gU7W9bwAgUeQKsmftFMtn5MuWMAi8QWoAgfSmmRqODer2srQDW3hhj89Vbm9qY8kHBkFZoa3AQBGOS1J/zDXEyYIEAbSPUf3dR/7h2sxMy23YVwYsIFyyYTxO/LXgizPkkjjLgaJ3AenBTkyQvGs456FTUPl/hHIV1hbWcRyP0uYMM9T4/ht5n1B9CK80Gfp6k69sP/gxyFI/nrfsnMaAJB07fE0GhIoIcoDxXcuU8CA9OCtJjkTBPVjU1X0A2E2Dm7MyTYNhoO5z4ebgWSVqjj0rvcAkODwzae+GrNr34ru90/LfaeZ5dku6d695vWNW/LX4x1Xv8P5S2D6nhZODr4GAFi37we1IIy+Zu1FqqGrEjQ6enf+NPd3P7maY4mFK74vf7rQh7mVT/1+i01NIdmzp/bzunA9pNzIuGXCRP4iTBjn2aU8M4y1pFMNdjGdqBQMCZXjoaD164WFju6H+ZKYMBr8GZlPMT5XL3kIAO1rpJzWSEU+8bgff6fcdmdeFVSAMKSiP6jsT4KGqcYeXsh/525qn/1tqrhkbshh7Nwqfa6MJwxxEneCY/+g9T0EhJz/GiYMKTFhzlh5BjrqkdFM3SQcxEJTFrsEwVzJJel8dLBm7BSMkHX45smX48pNn8NnTn+XPB6176vvzc+fc/evYoASmmHlhnz/kVbJlB6S+8poikAAI4FkIXCvvzG7G4OPfzzGfuPXl7z9L2u4hTtLBWEO7ZzF1V+4txZYLe4vCqhRzuj18W/Sc5dG096PnSLLVQXnwgDygS6uYH7ltjqK/nHdRfzAih5DnT6A/fH71Lwws3OVfuCwBpwyhwlTC2xnmWHCNIh8JkNRD2hpOTICjtYm660UqMKYfdO2jak6Jg6GVfdZzzw/6xrWalXhDwA8ZvhUxBoIq/BhASwIoyWxaXOH/Jcl8FfnfZ/k/aLmbkSAxoMQ0KC7DwLgfddbdofpX90iw5GNeSZM4ZY+Z8sKvOQRmwzwPNNJjSeM3FxrNVr5r5TJz4dOfjwIBX643fbJ5+15ClJVsDTfkgzjjcfn7xvN8j7S7WCqK+fD3EgnZ8anBgAO/DA041qf2kkAwOof78v9zWbzTDbedgayZTuABzSWQZjlWI6fc4ysspNDPUh5fn1H5y7aX/X7Hl73ag+90Yc/QyTdty/nCaMXMP369HDz5tzftCK5uBzLcawx1AhKy9rtSYLZnQ3jCROEcnh81uvzDA8iBDwGZwFqk5w6TpwYgmXCUGD3VcaI/Kdbqyf41EmyX3qTgJbh7gfC0CjCrS/9g9x7ReBj7UgjZ0zpDQwg++ZfOnJIhQdwfBv40GaTmPR5iElluEj6yJHJneWnFEEfmpsFYWRCQFQ5Y1fES056CTYNbcKzjnvWkrZfaow0LSgwGJdNJHU0Q/vZvYG9lq7UDqUkz4aCBWE0+8R3MKTGaaea8+2CE4wLu32YP5dDY/Z+KVaUDjXKCUN/eBUSJUGQKINcvW+oZCp1tPoDZdrrL6QQWQbR6xqgQSdoomjQVBHrBLGuAhYsM1W1XWUIHvcmc0yYyVWyP9fV09oglYaSwdXypuV5U88OcUEYxzzV83201p1s/jZMH9WWXsbMZNdjGe57/vOBoOmAMFxqOqvI5qR8CicZvMIApa/T+Vfacy5Sy8qJ/Bj+0FBu26wChOGOfBm80LAUIpGVEqNZ2gWorHweGiR40Z9uR6N9EMXYkFHMdlJkqUwmaN8AzYR5yfV/kdv+8k/cmft7//PkOTzpGR8CFUAnHscdJ7wQ8117z3MFZCTUx2Rvv1nAsoXqxFXasRWdqWeZMHXMFma0vBkErI9c48jhyu09JYHBKAfzgJWHbNVeMRmhmTCEMxDfN8bvvpKYw+Ee/ucjUuIr4wLElSMLQsRqPtJa2I+h2fsAQDLbmMWnBfj99q1rRlqOrOL7wjIDPKff0d5mlOdBaWPwrVbgvNtF5ue1+ufVfLN3yJq+NiJlcB7Iaz7kEJw8DmRXvtcUjOqK+qp4doURbz8QBgCajv+3y94CJEvSrRxlkzIJIXo9HFlxRuX+fNHA4XELzOx93evN64nm1aXtZ4W+X+TFPHHHf+P8H78DEweurpQjAyx7qZ30T8TRPkyYHc97Xu7v3a96dd99LSV699zj/HihYMFti8OEqSqkmP3Sl4/5t3NJGyc0COMyYeqSqT0nsUl5Ziq9e4uBMODIqGZ1+LUgUzfRyTEGpgsJlsCEGVpIECf95cgO/vVfm9djjBlZLgAYPFrW6nebqFluUWoTnFX9QVEmU75p10ei8JWbqO2ftRRc1yl6mfqvTwBwQRinKIEQpF4eZL8+jk1yj1NAcB8jTtI79EKsa67C5ID1fpFMGAXCDJcTsitPm0P40k/hKy/8NDhluGntd3Gity/X5qIcWe766sI+Kgt6Vp9q2e7zA3lAFrBMJkYzhEJgaFxJAKcjue1ajywXPuaij+zur0q4Elt8iSDyp//2Wlz7lR247ms7+27HTL9hwdsqbyVAgioamMiBMN1qAMB95jXTgLD+BV6kADLV+dvpthdBmG4NIAQA59+l5oXMYdv26Ze0B4x+PgXxSwxgHXO9WcOEaSkQRoiwlgljOxGOgeNONG+fu1ox6RwJzjTR6wBnEAfgc4GeL4/FBWGEEDilI8/jeUeeiNGfvgpx2gIR1WOHcJkwAE4UsnCBeyHSg4eQTdkiHcYFqOogCOWy4Aq6fX4JUNHydbn1rReCnVDvbfSss9Zj3UgjJ5NIK/pp7cHCKMDVfHLjiiHAj4HC9jtuOgIBCVYDwMBofg7kKYa7z0Jcue9KeTyqyVSw3PxI/br8fx+2KwDsKdRyzn71q/l+9VeY4fdgxzIIsxzL8TCISPkj9NpyoPCKEkFOEKfjnhkg2D9OjIHpwz10VStLufW/6VNdv/HjH8v9TZr9zd2WYzmWEm7FXgjgQ/sP4ZQkRW8mMJ4wvkqurNo0WPi2wMm7hWGkM5rX9Qckk0I497W45iPIVFJpdqz62daVxwAwsmArkOuSPybGV5iXf3/Oi1Ty38ZQHKBn+hMC7L8FaXvSJPQ2Dm8u7VIkmUkghyyG31HSSRVSGrkoTPZoH2ZLqOQlQi6Pm1dRACriLee/BV/8tS8umtA71vidS7bhDx53PD7wkrNz90cxGs6C+yf6mrEQcZj/TuRTM7mP1dojTbom+R539ua219JVesG2e3Y3hHDMdMP8wnCgZc9XEYTREpe58ENkvqxw6oUjACwQQF1fisLvCAC9u++G6FnfE70QiqJhQ3nX+zJsFcZNQkcDNb5HLROGRkaLWaRajkyezw2jE4h8iif6PwQgQRgBkjOKFs7C0fNDRPt+ZD9TbZn75rcASDmyQ60jAGRCPdu3H8m+A4AGYYTArLNIz5S0gkAGj1aDMMc7xt0isSyh2PEhMHJk7qJbM4WUtFQY+AD1AcVSCEU5wZBlHXDFhGm2KAZGBzCwkK9eA4CtGcX1u6bA9v8EAYsw8NMT0WuniL0YQRahlQ7ntt916ySEEOg05DF2t8sKvJFTLsMtmwiuOv8vsG/to3HbtDVfF0qOLG3M4O03vAwbZmTVO/uv/yi1BwCYI0eWBAxUJbqzmoW/qb4UDOfuYXbOwKv7QU89I2NehMwDNuz9HgBgivJSlT1LtP8LB/F9wxRyq05n9iyo3xMgil1ClSdMuHEjFmJZOXnO9f9o25x55v4Xi/Rj/QCagdBX+fH8nEhwLiWy1PPkegamVAGcWV46cW1HAivElEEScC+f4LhlXkpTZLB9WqT6sexwGfTyOJCCGMApCOuLYs447WWl95pB/fxtLB6D71yuhp/f90lrBqvskrD3cP+E2c2n/Q6YAtu6t91m3r9j4K7StpMF+ZEkOB0DC/sk0BhUjwkNA8IsNn4tfbm98IMfLHnbukgdBkuRCUMIsQUTzpgdn3oqAGD42c92tn7gEjCZliPzCCLtCVMnM+i8TwQzQGkdE4ZzC8KkvrqOhNYmMDXrRRABTnXFcB9WUFf+PuWp7ZMWMfAGgFN7Cd7/DIfBNV1OMkY9+7v+sJxvRmle8tCNv3xRzb3k14Mwbr+jAbC2eY+g91OV1NQgDHWKEghM8QQgC5FWzAqTm8uUJ8yJq/Nz5Yj6SHwgTLUnzBDu2/I0+Vkrn6wd2z4P/xlvA4bWYDAcxBtGz8YoY/jTc94AADh1nRy7dHJQg/+5K6buZaqu5znPPw5xR477s44kmQ5v4xYAAKMpxqNhjE4oPxwemz4DANb/8z+XvlsZv8LJytQBYbJjlCM7cM9M3881CONRgjjoD95mDggTN+y41q5hq7hSX3oe4GXHyITp0/fPdVNkhSK9Th/mDNNjPEtB+CLeUwAyVejU8x0QpqY9M50jBoQZGpXsd8L9eiYMdNGVQDS6EkSNjyM0A8Dxmet223aoYyIiP4b6zK7t3MR+xgVubRD4LMTstZvgz2/H9kOPwNn05komj5nGqPljBLd/DHHgbW+3f3Nh5hI8IoAfQxALwtDC7Wl7RleulIA2u842hS+pMXXYYcKYHRFqj1VfQ0qQqjnsysEIoH4Jstlz5zTuO36D9AcTHM3h/BxIryd8HoLr4hrFriGcGc9OG6qPdDwvB5/6FADA/z7S/vpHn1j4XpZh18v6sPs+/3v1ny3HMcUyCLMcy/EwCF8vSNqaCbM0EEZHQOuThg+n0NWZ89M9cG0o1qdo1C/ILdHGMgizHD97DMV+zvz4kV072TLyT4GegJZv0N/4NkemVrmcogTC+JQY2jQnPgQHmCcnVAuNavCAOMnbMLNMmCgu+5K4ETjssMvXn4Wg0HcMxj46TlKef+3PwBg1E7a1g2Vz5dVvfpOR/xjurABRU4Wk9cCBvUUGjjgGWcL7W2neLyaGY7zhCSfgqaet6bvdcDMsvcfS0ZKE2Vwvw5pJeUzH7ZegSpZ0DUDABydy2+vk/r41suryqZ99ah6EKey/ccol5nURhClKaOlIYim/sesxSgJOs2ycBGMjKCY1CUBoTo6sozZx5cj0cQmd3Or1DDuGqwVQHEU5JkyqFhUilf9m6jk6ceU2xIGHmM6bfTMvMpWuAMCdhZrvhYgdsEQf1+yXZRV3L+NIlLmwTqDteOkrACXhEEEaa5rzpNYsjKQlnyKdDHQTYyy1AFXsnEt9TV1PGCNHps5NGFCAEHiqXUFKMVeo2mRZx8iRNZoe/Cg2z6cbKxjFt24/iOYhD6+85u/R2vUUfOQN3wcRFGvmrITVM9uvRwaBtMcwe6QDX7GePGd8vXEzMW2cTeyxzn3jG7JNg3YBDgDktptRFWnPJtsST4Bw7fFS/bxr01vKGeKx2NxzgpU9vGQo8Mvz8DS23i5UBSklaowcmeAgnmcWyq7+OlXXKmMCfmYXuJoZ0tKqHxCgmumUeRBUJlpEcYVfiHUVFdnmtynBYFTRx5Yk7JznW8gKUM0o1MGhWTwqseCFoDQPou/pLuAnh36CJLXHH2k2TjefJAUkCPPlgZbDhKlnaQbjx+X+bhK/r4/XOx71jtzfRXbp+tFGzsclPlOyX664y/aj9269CsedvQLFuPzi9+T+ZjXtMDkUdV/0dltZrboxR8uRLWbObL5NyJKStbxT7bG01MhVshaZMIABYVzgQevXk8iRilykivZYImNajszxhKlJpibqfFKeggAIE8Xi7NRJ7WjgnCHx7H2e1XhPaTlkDmZkL0W/ZOe8LGDweGr6sDpPGDeuaDawY7Wj619x7UeV+gtlCbxBOTY3nKQ2K7DCbtlc8xx5tu+4x/EM/fRFBNuI7R/MvWxueAKhzMgtE8b5fRAkBSbM034skCiQmlMAwoNfmHfc3d6PiWkgTOQBaqlIACCn5/tBctHvARe9zvz9m8/8OC5/4Q+w9ZzfAgCMqXnXQsKQMm6azpz7k6j+2vPke3HkYd0+Kck3NWor+c1xKb/FjGYYGN6CwUaMTBVDJI4cYp0Uofl8WaInB8LUsVTqwu9TdAoATFgQZjEmDOMCOr0dRh64WunVsU9cEEazTwT3ahl0QliQR0fSp++f6aTICgUF/ZgwCwOKlS4y64dX04cBdk4w01DjBfXQ6ZTHbgDo9ebhK1WDxoCc63nCqwW0hAPC+M1xeCqZ31jYjZelR3DPvT822+o2Dizkf3ui5tzo+2X7Iet1KQjHdU1P+qsU26LBV7XeiJEYmTPmxejecbvZVoJ2Wm5bqCSTBmECeIXTqZeeRaUH36s+jwBA1Jg6FMv+cq6TFm3mZLv1OEOBDCGeeLKSTqN+Kfl1xzVHsGfti+T3+AH4jQIbWDFhAh4i4xnmexmoWiN6PAF3coODT3yCWVOLzB6HZsvMNQiG1Xg/PVDuv9rXXlt77PjJf9Z/thzHFMsgzHIsx8MgtNSMptr3A2FOD04vf/8XhAmjkfmZA/MQagHT1wai6FfB+i90l2M5lhKVTIdQJ6YVE8ZZGGy/0E2Yy/s2deTIQppPzBNCMDegq/8GkKaRkX5iUfWz7TJeLvuRgK9ywo1GkYmTD37qafjGxvPwgdN/DZzQEhOGUgIG+xwdID7SzZea5KZX4dvSuugik6zeNHUSMqWRnIUVyZz7GUUQhlfWOT/8YqAiSSp4owTCrByMctVTT/3sUyUIoybvdGhVbvvh7dL4Ukt3AIAQ+j7NSn452x97ElZsGMCZj99g5MV0FKXLdEy35OLm2mg1TvyXX8e8qhQMHOBAy35kvpOc63ZycmSpStBEK08C01XE6h7jytNj9itfASd5JkzoUZycys+ZF1kz8xlZCUlURX4Qhoh8Cp8kRgc5DZrw79xh2pQesdrwfhgheqpN4mrwhyrmZC/jWLsgk/h6YctmZkEViyAUJFcNuKunElIky8n73fm8Sw34kTka8yxxkthVIExOjizPhAnUM7VppwJhmFdmwhy6HQtD0lw7HvBBgiBnDq9jiBN0U45z/je/6N9/zwyeesdvm7/nr0kRe7JC+MgtOxGovuaHexbwux+5Bv/1jmvwO1f/k9l+wJ+WbXbG37Wz8j79j8fKeyI7+7xSewAg66iEqODIfGHkyOqYMKkyjCWCgWyNkIRDldvpMD4hPkFrVpiFqo9yooYpQIiI/G97zCYYfWV0m3GOsGtZOVWJNn0veCwyoAen/RNQzz/x+X0/H24G8FRiqDGjK/TlPndveBwAYPqwbe/qSeW3xPKJwgUlX2dAGCFA/Hz2KGQN3Hb0NqSJTVqHqh9rXnABiuFx4IpmbJIWYR8mzAVr8t9vLbLc3Di0EUz1PXNxmQkz3Ahyskx8riwV8ogLT8TjX3kqLr3lr/GY77229reuXHMqGuqcvuvQEeeTwpikfmP4uc+p3ZeWI5vvlWUEc3GMedrZr3392L5QDLctFaC8uZ+5y9KTHQENnXtpsap2BeasevObq3/biVSdc58SRLqivZYJowAhNQ+JFIutTo4sM30Tx0mOlNeRPfOV2zMF2nDCTWGA6AOqZG25H8pTO4YsgQnzuIV2rvLa4+V+zzBSRWbAXuaA1OJ++N997Rx7zQ+MEvxWowxCwGGTiIWjqi3y96mTqcwI4HG/9D3akb/BKLBprL6f1iCMG8Mn5/sr0igXG5GmBd4HY/v7c90Mobp/55ntuyiTbQ9UBVPkexiekV427UZ+vrXmHe9Ad0HNRWiKMy96M1rxCNqhZe2YdhTXoXXxq8yEcZ6dOk8SN9xnp8jyLm17DEwYJgT0MBeEnkmr17FPzDxEcBBTjOGB1/QFLmjD1HPQ6wOqzHYypIVihW4f6cpda+W8kvLMFK3Wec4IIUCVt+msYm1wGqBTUUABAEnWMZ4wzabuc+qZMFAgK4EAiYdBhHzWtu7ZjomFTXjuwe2GgcjUPrRklo7Njp6p+3jo++VRO55r3vN4gAxeTubMfNeMQ/Jc3zb4aGRUz8UbSHfuMttmXBiAQxM+hfLsy/wGhtv5a6s9O4sUJz9XtFLDhGlqObIUbkpde8J0E+0vKZmHen0FLzDrH5pI6VDOBDKySX2fAQUGpC4WCliMj115L17+79cYb1vKUwhC8MPTHwsACLdssW1xQBieaiUCYIX2zDzGGu5PDQ4A07sX33A5Fo1lEGY5luNhEHoSYuTI+oAwa/1y5foDbVL9YMXQnBwo2zM9IwfQxzailPhIdi93/Mvxs8dQFQhDNKiSlyMDrHQWYKtmsrHjAcgKlyoQ9PC4TCwn4TCmDozINwWHqANhClJ7mgnTaPRPQjbjCO85+wX44tZHybZWJFwsM4Lg3weGkR6+2yzsiwwLACC+b2jxw70JeNr0+QGcMRT7uKV6wjwsg8WmQk/H9gkXPJP3Vm/2iEm4BFEeuFsVySRPLx5DJ5bJh0R7UqC8CAsiDy/4s/Nx0XOPx6dPujT3Ga1hwhwl0kdkAQNgu64wVVSBI7GmFwlHV+rjIeCdjmLC5CWX4nDQsFw08DH9P/9r9qWBRyM1t/2paBK5KMv82KxrZr/6VbUP+eyFUWja4SuD4szPM8hY2y7ufC9EPDhhgDwNcBDFEutlHFTLXDlJMOKCMM6ivpNqrXiWY8J0V4ybhJKbpMlcEMZ35ciqPGFSedhEM2FsJZs8B2FJ0zudvBeUyfMwOByCEAKPVYMws+0E8wMbcu9f/gVrqjwwL2WK1nvS0+TIT6ws0+2TIU69dh7TuwuJS5UMEE6y/qdr5LU6qk6Df/2Pc3Jx5quGCcOR+pLhAgBZHROGa1kLhubaQXMOAVdyyA11r3sEJG44TBigVziPXLVfAxN6fuFVsIoYF+Y+oqI6yaJZUaPTMxDq/lqsH1uMtTwUB9hwUB2Dqi4VhYTT7tut/jkRamFekGvrEOWjpI+12YBXML2Nsgay3izmDUMoNTJRVT4IF9whIBw5sjCsl4Rc1cwnPVeSxQuFrv6zp+HmTQR/+WKv5AkzFBdAmG63JB915pYz4XkUQdoGAdDKpOdRs+CfdOH+W5Cpa3+mm7AyGk75/fqO5Gcx3IKOe4/0MfR2+pGlpGpJjfyZjs5NN+HA3/wNWAUYVdpXlWdFhRyZZjHmJuUVoIHZXgjzfX+FPUfz37u8cnvNhPE9asbLomSgjnRBy38pAD+V/V9nrloSdfhHUiM/yBiGnVPyjY/eWrl9likWJuFIFXOG9ANhOmU5Ms4Wn7M8pt1B4pz+pMo3QPsX8AzRCSfI38l+NnaF+5uCAKvj8rPqFokkIKDDw2bMpg5ytC1JsXpuS+n7OhgFTls7Xvt51MvLTX369L9Hs1kAYRa5332PmgKYmU6KUJ2zeW6fXy+T+9RDcBxQM16nmtlCKU6643aMPOfZ6M1IyUUfCaItj8aa5ip0Ag3C9C9+yjd+mQmT5Jgwi4Mw7nO8KBPGgDDU+hnVMWGYMCVnQUCNWXkd+0TLHlLB0I4cBl3NMTDndxN12ZM+zJ+5bgpO82NZr1e/vU60U54ZALoW5MkyKV0FYD6yc5hOu9r/Jum1ESgmzJBa03rCR6cGFDIFLpQD8XDeqF7FnAKINOOQsnz/LGtShN6hbQvj8ITA3iE7/wxYhA68nDSwaUuBrXJYTCDx9FoiX7DBhTBt10MZ8+X4sdBai2FnmObczir6MWFEYfGrgdmh2MqREWfcvGr/VXL/ij3EKNAhBDjUld44NHAKITqYjfNzlGZyLeDL/iw6UQLoYU/2Za1kCHf3voxrd07lQBgOgkS3kzGYE57Z89ntab9L4HhF+e+UxR36xl+vGAP+u49c2XIsOZZBmOVYjodBaBPw3oKWI+s/qXvKpqfk/v5FYcI0lD5vOrtgDPH6gTDFoAMPrA/EcvxqxlChqhYAoBfWShLJXRiEcbkK8J6VUm6FUVJZJZ2GGoQZwp7ZUQAyKUujegkXNyI1b6JxfymEIcdE3qek0ofFNeybAwWb2d+XCUNbLaw6/BP7t66IWuRZHXjMY8zrsVe+ou+2RaCA/wKBMAOT+cpowSNT2atDJpn0qkMBfDNTFmiI8zPfdb/7cvN611rJKvAgt6GLsIRuH1vEN0iFlhQJsxjXHrzOVNEFvitHJo9joWUlS3i7nfOE0dfK92NAS7moJGt82qlmX1raJNZMsXgYPSULlvmxkf3RVVtGHi2KDLNIS62lBT8JPX4IcHjhAEK/YRg3Zj8jIwBkMl6bZuoqZgDwqFzEBYLmFvUe1xW+DL7zPImwgSiRz3Uvsv4qmQNOaCbMdy84xXrCuJIMjOUqm0M19n/mQsWa8ZvoTueTVsnQBrOQHxqX13rfRHmhGoDgpHt6ODJ+au79mXtsJfKZN75f/u70JABgTi1Ibz3uPFx64OmlfQKAUAlQkdrfvPkkmcDKHOxx6r//u/Rdpvx1IDhSz55/d19u+Orcp5Rh9LGvxtDsDvNZVpmMUH2TT0DilkkUBCC493ABTOpaQAgAmhdKoMErJA6EEMi4MH2kToYUo9OQiedud9Dop4tFmDCLhUzq5/sNzVCIuvKaPeLXtprtCa9mwhxUz5lm/QSrVsOj+fMXZg2we76L2YUFs61+7kjFxOzxN3BsYwQay4kqqtfrYnAJp2XhhHX4qxd72LmalOTIimM2nz2C+663vlrz6YcxOCrnh0wx69bvl5IaYW8yt6+eHyBVY2LslOfWwSTFAgk34sAzbO7pmuSX3Dmp2HN9eMPDfT/f8fwXYOo//hOH3v3u6g2cH3KlTk1oOTLn+IW6z9yS5b5yZK4UlFOEcOhd76rcXHvCBN7iFe3JjOyzKM8gQo64K5kaU4drjJu1J1fCsWezA6jPVoM2mgkjCAdVhQEVtQ52e5WUpcz1hKm+msMveAEA4MggMMg50gC4fqu8/k+690coYjeeMgKnPDNglri/IMxT3oUnLLTBnceXAGhF+UTl51vPQ2o18pAIAjBmx+zMMk3/7vBR8FW2bycFxkfAgIZfnoM8Ys0jAJSr44+29iIqVHn3l0SQoQHPa3dMGibMzR0necnkfR6GSo7Ko8aPhnkRfv93Glh5nQUI9bAckAwgBBEh6CnfvKkRWWS16ROfWLRdNh56Jkw2NYX5K67IsVR/HpEeIwjTdp7LbBFGmQFhiPUzqveE4aZIwPepJnPUAhmdffL+ITxDJ2TGfCSrA3mctmZ6332YLQudjlk46XlEN63fnkMx0Xhm+pleHROGc7Oe6Aa2KKezUM2ESR2GYHzIFnJ02tV9pAEeCIBosBKEufJumdNhibpGLP/bNCMlfypA3i8CljECAAELQWhWzYTRIIyaP24YbiD1LBPGjYwJc861p2OUbAYA/PT456PhHG7meP8VfV9CZ9znxRybGj91n5Qygc9ea330fvebvyvbneo1CTAxfQZOvL2DL7zvBqA3BzvjELhm02dyu/ezOwHlT7vm7W+Tx9CT16yVjIBGh/GUUycQe/Z+EcSCMCLN7NE4/fV8R+6DUeD1U9MAgIXGsY81/9q+G392xVv6s3+XY9FYBmGWYzkeBqEr7fXEpB8TBgDecdE78L7HvM/8/YsCwhhpmW5qaL1LmHubSO6+58Fo1nL8isVQDlTRiS4lT1HwhAGAqFkGYZLvSdPwE/dWT0I6al54YOICZHsks8HPOvDC+oT5yj/8Q/NaGwt6K+qrcAFgzbBNVhX9K3Sk2gCdyAUzE8RMUos63jqG5nba/apkfZWsiRsjz3ue/c4iYFNQYATVsTcejtGbPjP3N6G9khwZIMxiUC9CRLtnJvNekO+z4202sdpQ1UrbDqgqp0VAmK2rlgZOn71OJp0j1kDIiTE1jwJ7T2qjaXfhxGZnwR05Mg3CEErhw3qcCBDQlm2LBhvOiE82721WCf3Ma6CpJL00w6KtAEUvDBAHHt6ZvhARkYl0V/4LADjXIAyDHw7A92NkVFWdqQRPdLxMpHRdEMZJqEcq+dbgIreoD5QkFSPMLOLkFxqWmeMkiTPH96QRyWOYWrnSypF183Jk7oIuUp4S+0Y1w2YQvX3WiwIA5udigFBQlmBgrZJoIdUL7YkF4L4tEkwpLogPtG5EmMrzSQ5OAwAW2gSZF+Pg+peb7b7VSHDhs6Yg2A0AAJ4orXQHONHgS+qAMO1rrEa4DqYWoQQcmedIgdUkIhqK8TPfYmhsOA9rDlxlPku65efAACWBBxLGOUBloZ3/DTotnyvNDqEqOe3x/HnaM9VBxoS5f+uYMPrz2ewpJmGxmCcMABw/enztZ4fn8m1J9+83ifBYLcIHxty+Vf7uGfsfk/veYQUG6YTp1Cc+gWaST25ErAGedY2pLgSrZFLquPw0gpOmFuCp5yNYtbp222L4BQ+sqrh6/9XmdacgtxcHXl7tp9fDoausgX3bu9l4s/kT+reUXIzjiQYA40+2Cajote49q+Xn8u3Kjh5BvzhpjaSDVVXwFnatmrU4yLH7Va+qTaweerf1uKmfEzvgUgXLgFQwYbTnlr9ypd1LDVgKANmhQ+Z1tH177XY69DzEp3RRb4ekLfsBIjL4EUeg+t1Ou7rfc5/V1MnJjaysnodoEIaAQVDNcqlnt2Qq0erx1CQCWU3bxYBkUVx9IkEkBFYwhmZPJSkhUCyE1yAMIcyAWWffK8BIdXK0b1zwaixEA3Cx4J9sJWh6+UTlP9GX5kDNRpeopK6an8Be9wnG8Mbej8zfvOAx1PMpmlH5Hnv6Vgvqn3rLv2HiwFW4b+QtAMo+ikvBLw7Nyeu0a7Jt2A5EEEx2J3G0cxQBk8c4pFg/vkdl4ZMaE9qNQWNoDQCJAguCQJ2HyfswF8kx4vDoGG5+wlY0zz5r8YYdq9bgAxj3Pfs52P3q38bUpz71c2sDcOyeMIkjK1jn26TDZcJEizFhuOUsUI8gUTfKwkyNT4pKRBPBcGSYmPlDVudlosAcBg4tdpb2Od5dh28z+9cSskkf+TJBIrW9BXuTuvPDmJH8bQcWYOl2qvuNJLHbzP7LNyHU3H2hXcfgVHMrIgDqgwq1RnbWI9fuVEl91V14BaCGtqufjW7SAyck52UX8BgpCGYrJCe134n2qhKjJ1omTKFgI+cJU5FgcghPYFxYn+VCIWDzzEfa3y8wmLODcuxrhh5aas305VvybBYA4MyCMKOdzQCAw7vmgGTO/C4RAjtH78Szn3UUEAwrD/8EPt8JBKofU+Nx1JsGIEEYCOCsjSPwDGiXIKMeegpQEllmJjJunxdmQBIM4Pju+/G9A+8u1ftUxa0b5b877bQAHxgdwRfu+zJuOnRj/ReXY9H4xcl6LMdy/BJHpHQlNQhDF6HnAnnj0l8EObJt3/qmqYhi8E0Sreh1sBzL8WDHKWuH4RUT2+dK5kaVHFlzqFxJeva9cvYyvFC9evQ320rW+ZaUEIx603m99UIM/9qzcn9PN22isC4mHBBG1Kxk/YZNjAcZkAn0ZcIAyBl/e5DfX+xRbZ7v+EIsAqpEzQCNQTuxHW+O9t/5wyhOHi1IqYGU5Mh+55JtcGe4lAvwXmZZGo2yn0I2IftE7g0gSgSYYpB4af9kzP95+fk47tmSTfL43zypdrszN0gwpJHF8IU9hsC5xxpas9joxROke/dCdHvmntkAC4gExC6YOPVyvl16e98BnDYQxaDwG2YCyubzpsB+5CMOPHyKXYqBlpIT8VtYON0CVZ1bb1ftE/DCQRA/RKoWZXeslgbAukrrm7cfgsd0NaJtXwNy+6YgOVmcYSWVxSnPecKsTvdZKRpnUZZnwqhkYNg0453LhBFZas4LAIRqrD/O70mAglCwL3wJbizMKTPVzmH4oyNykdnNJ6kvGfoginH6TR/I/X3f8HdsO+fl4nm+Q3B0/BTz/n8N9PCTiOFashFESDBI+xjphGxGPYyl8vhdJkyVJj7Txy6kHJk+/ySp9tDguiqRMDQGV4NAGOArrawIVYtt30dy5omgPIGnkh3pfD4hYIA7Z7G9f11QAqs6SYaMczBldF30kNExpPwGBrp3GhBmMU8YAH2rBx+zfVUOBOjdfQ+gZN60lKHn9K082Ghep117fqhOoDhtX7s/f5xR1gS8EEKZjhOklUkLHYwCmbNaj+P+kj3/cMk/mNfB8IY+W8q46chN5vWZq84sfT4xZPtMlnq47hYpebZu7+VIPZjq+qEnPRGABaAEIYhOsiDB1RMOEDvmsIrMq/z1YZNT6Be6GrbKUNjsu2aO7kr8/fEr8tvsePGLK79z9MMfdnfct20AQPxygrxSjkw9m/7q1WicJZPPolOWPdSx6+W/aV4/+T9vW7QdLhNGFy1847YDldum84rxIjJ4njAJzMkd7UpZQuNXRjlahGP86M0AgE3bq+9Rpv1vwAFqn4s6E+wplaikPLFMmDrAxvh+Ad62xysQxn5ezO9q5iUVGbgjs5nSskziUiIZWI0FJy/ZjoFmwZfieedusPNFzdJqt22BQJQveojIPFYHd2Kgs68kL3ZgqFUp8es5YM2qIzfg5Dv+E21fghxFpttS/FReeJ7s65KMo+GMobtmd2GmN4Mok/PU0QHHzwVAoAoPGulAru/VcpVjuj/1Y+xT8ki91pn4vsM4LLX1qn8Fdvxw0TY/mCHSFNn+/QCAyY989OfaltTxSlkKE8Zlmuy4qT/InRkQxjJh6n4j48IU3Lzue69DL5BjW7ddXfSRKW87yhlmmzBsjzpgaGZSPpOtziRWzspEfC1IAqAxJa+Pl3Ucj5c+IAy0vFRmWMO9GkBIgqZqvuklZqyn115TuX3qFAtRnhkZ1l6nup/RwDahALzA+O1B2O313JYpWl1xLkU6FGYt4Tx7nVT2cz63a4+ARUgIKpkw2rdFS4Y1okGHCVMAYYQtoNFFVDu3fBoAMDx9N2K20gzxTAhn3/k+qKGY9ADAvUL/puexhOBrr78YQF6ST++KG+8xoJxyt0yYlBCsvvq3MJG+D6fd+hE519NjtJITjZJpAEDAQ0RZEx6lgPJr9XgGRjz01D6lNHD5vIt2G/dtfioAYDrbiFXzm3Lt1RGfdpo6AI5Bdc4//4jymv57132g9N5yLD2WQZjlWI6HQcQt2cEvxRNGR+AMCovpjD8cIly/3lSZMOJbJswx4EfxGac/GE1bjl+xGIoDA8IIABhYDTzxrwDY5KorR+aCMO3G0qp/6Sp7Yy84IIzfhyFCC4n56SWovbiVy3XrWGL6CiKZMCA2QV7T11BhjSGJkAvyxeTIvAHb4KWYmTYGnQl4VaLoYRpxEbgSfokJM9IM4c5sBzoAS5NKppXZ75A8f2kwgJd8l5uJf10SWEcj9PCkJ27Baz/4WJx4wZra7QYH5P21eu54/N5qu13g3uthmVWZ7tsnJcl0tbGTJQ6FXTAJ4mP+B7I6Pdy0ySaNnWsbELlwYl5kTJ/5vKrEU1V9nk8R+RSTGERE5WeZH6O9QbLC2OysvdcJhxcNAjQwIIwGr/Tpb4UeGmpt6MqRNRTLxs8inPS6F6Nzk0oCc9nuUZHm5Mg8z8fdZ6kEf8M+x8xhGAQGhGk5TBgnAcCYkW4DgLZKJjzK328NjK+7AW6kaoHrZx0QSrF3qoOJo/nqxTXhzbm/CWcYnbkbp91iE7YzkWW3xR0pNzPdbeLWkyUAfe3AQexTFLx5b9hWy+tktgZhiIfJUB7TdMueHz5nZc/Me6aSnoNTYqrIA85ziRsTjklqoCR0PGUsWs2EUfdYEICdf6ZMuinQJikkXoRj3q3DZ6QkR9ZuZxLoUvue3Gr7tdsvXm9er1CJ3mjOd6Q7+mga6V8X9UmbZujhwLjtr0WWGraETbjYc065vQ+O7rHn30hsuL/lleXI4AU4OC+BUVEoTBh+zrNzf/sMuM3xsorD/gPUmPK2AgB/CXPUt5z/FvN6yPFcMvsbqC5gyPwGMg8I1XOvmR/UyMQQTLznPRh/9atx6O1vxtdbNsFMCcWH1j7VbAcAPMj37yte87t9270UEMYGyTNhujahtadAeu3eeBMWi/Y11Qk3N1gVUKPlyFxAQ4EHxPcw+LjHAkAOFCjtwhnv9xVkv6oYNK4njGbtrh+tlnpLzXlJ4Xkil9y7/Yf7Stt3RuW9NjnMsEYQtBZk8jPtVV8TDcJ44Jj2OJjyLUsr+hgAuHdBJoopTw2QzGtkiJgGTSkQNsYQCQHmTIeKP+Gp8YaCoXXhI8z72f0FYcCRBAS//9seXvu7HjglCAqSPa3IjkFuL6wr67PhdbntCQGeM/YWvHDl60rJSsEaBXa5DJ/4uPrE/L3XU+Np0fNpKSCMfs66KcOQThCD4B+v/Ud0WAdRJo/RZeP+72lPRpjIRHszHc4VKulxO9bXxo8wF1npQp0ozkXaBXv7CO78zp+Da18EDWI9xNI86UHLREv3lZ+JhzJcJkzR064qFmO/uMENCGONzeu8pJjj8SEIR9eTIG9Sx2xpa+N0hnVHhSmcqWufBkQkI071GX2Ot6eKVfysa/bdjwljPT7s+qtX04e5c8kndrqmXxr6+hcrN9c+WEQwEAhQLvuXbq+un3FkqL0QgerTI9cXRxW6aII5LUgPki6tLA3sdOV597mdF2ydPAM9SjHZni9tb66q6qcH4mEjr8wKLD/GuVmsUrUOPb8j/YRnRo7DKrwV5+55ClKWgjEBYhg/+WseBbYfoaxwDZwxdd1IA4RUk/m0D2Hm2QIjQHZ3TeNzI1+kxGHGOvvXnm4ezxCovqyVjOCR28bNWoXyVDFhVF/EMtvXuf3SzAL2rrvE/DmQ2HkWoz64VjtQErXoTIEwYo6hGB87/KPym8ux5FgGYZZjOR4GEbXyE9i66nQ3XODlF0WOLF4vZSKEILq4s2/lZTEGL730QWjVcvyqxWDsW58UQoBzX2lM8IwnTGiHR9+RMykuQL98cXUS4Tt7vopQ0Ye7sTQtDdMFhEE9COMN5RNPIxuOW/RY3OenrijW93T/QLBqnoNxh6XQh7GiK8qpNnleQr+kIz6pnpGhI3DO67H0Az/vCIhA7/DjnXdIyRNmOFcZSrDloMCWO2f6gzAjcjHRjcdw4l5htuUVwMj9idExe6+KbMS8dkEYbdjp3uXsyFFkBw9a6SfnUgWOkD6nHua/9W35fc6dBLltv69ktGaHt5rFgZ7wC0Wt93yKwKPwKEVINAjTBFOLXjY9bRZZAhx+NAh4IbISCCPAuMCV9xzFyfcoQFGtFgcuvRQ7YrUoRQPh1CT2vfkt6mva+4blJP5YOABGtSSDvXeZI60VKjDOd0CYLOEmQSPSzCTTGclwyYmymj+ErfYuyiskiWKtsR7geZjppLipUKR7VSPCCcP/bv4++Y6PAwBWHrkRz3sm8Jvvf0SOAdLsHEYxNp/F8cpHSUZVknEDwnCn7QCQOX4he1Y6/IGKynCe5YEP41EieKWuu2kiFeb3NRMmqZCpMEyYKISv+tZKLx64UkO2nSsPJKXqzYW5nqy+1YtmB3Tcd75lnhh5kbBhkiGcLJ6Aes2ZrwEAPGvbs0qfRb6H/SsL7Ec1WTIgqNMPNzKbNGQ9mwChZsHv+HYU5I0i1kCHEBwksdqycL7m80Cfz4ArVKEAB9BwkhRV4c5LJ7uTfbaUceHaC23bKjwmQsdgnjnPyODcbqS+/b10n6o+7irzdRBEd/wbVr3hD5EMDePaRv75euQT3okv7d6HU1WlcDyQ72+jrTUV8Spcc97aqBnfeE8XJgGsYnzd9YpX9v3t2nASL8/7yI/xzdvyUinmlxyQzoCUlIIoPXzervZgAQBv3IJsnBDsPMcmd7KjR0vbp5yDCuDQrZPYoozZk5pkaqZAGCIyeJtPNcknALjvxnLlPHf8yoZoZNgq3T37K/fP1bFSBQ5nig1TzbYDdB0E5cwA+awmeal9IwQB/MYIQiGvr45OoYugXDHuwEFieW8KSpDRagDs08/4dOX7OhL1zB8cIzg8QnBmtwcUQNDYp2XdPThsVPcR2HCBbB8BooHy+RE8LMx3ZAxFQ5gqdBEahIm8CGvf+Xfm/eZ55/Y9JgDGR+jjP9qZe/+Gwzeg0+si5Oq5HlllPvvKWU/F/iF5MGfveUK+Klwlk4NUMd1OuQxHWrvN5zfuuaXciO/8Ff5mfBTPXbcGHwoTUxH/8wiRVEts/TziWOXIisb3/QCszAFhFmPCuHJknDAzX6sDSpgBexm2HLTJ9mL7dKS6vxLczCWTviCM8vllPevx0u3DbBfWH0pv36nZnmeZYSmvpsIy9FCRLQeQaWlYzXjMdJ9XPW4JOEwYGpj5RxpaduFVNx5EN2XgqZ6jFtraq5Ej66kCKF6Y6wjgpunvlL9g5mLy/A82xwwIk/mxYW4CkOtb5JUePOSflXP3PBkpT8GEMIU/xbV9HA7h8q2fxI1rvovh2Xvz7fHya9fByCnEgR1fuVIx4NT61ADAPF+BM/V1VWNwAmIGmpzPrPNaF2od127gpDVDIGrsoCwFIxRdXcSUZqYR7rOVzufnNk0lB8cJxVXn/wWuPu/PIQAk996LbGoK4sp/wsQBuaMT4hEUI/g5SjH+MsQyCLMcy/EwiKhRAGGOkQlzsF3Wonw4BrvP6lfr6l56DInd0Ze85AFv03L86gWlBPdyhzHg24mgkSML7CRr5Xo76dx6X77KaHK8Wi6sx3rwmao0irUnTBtR0F9ezI2NaxcHMgDgiSdLds7zz62WfBloSnAnDVp44o8B0fWsHJlf//xpSTKiQJieqNMOtrHl85/D2r9/J1oXX7zotmFsz3EVKPFwDSYIkiOPtW+IshzZeCvMyeH82ad0NbtlexRjfIO8z+YGN6LRs2BCb2S4tO39iYn1I+b1SEfeM4wwBE7bTYWspZpg/vLLwTsdC8I4TW84C4RO7IgGc24WiC4Ic2PDnjemFh9cyZERzYRR90LkUxBPtmPnpieZ6mUSho6kF0cYNADPB1MgjHBAmP0z8r040ZIvcpEz/73vYXre0SX3G440j1psUgrfARx2brgM1zdVss9Zr+mKWiG4uQ9WTc6YBakQ1j9AsMwsZjnJcPZGKcMXCWHkIdJClXBmFrg9EM/DdCfB7rH8onKaEgyE9+CahR/j3OveidWHrjOfxTRB6IW5nJtkullj8zT5Os487WyT6OqmzGhWC3WdhJJWYM4NMMQYvn2G3K599dVghcS9rRZXFaTqGjZZWpmsMW1UYMYnn+AbsKNXISli+rEwNEl4w0AqVpwym0BxowjCtBcyVbWvkqPOiYtcBrIGh6KmvR+XIEf2pM1Pwjef+0385UV/WfqMC2E9pAiAnVea5Lhlwtjzf/ahz9vDyxy5kQomDE/y88Qwa+CjM7dgjcrB+MgnY3p33ZX7e7DjXp+KavZCaGYKAFx/6Pq+2wLA5qHNOH/ifDxh0xMq2d2zDoUgdQCg9Xu/B+ERk7iY++Y3ZRP1sRMK76p/BnrzyByzq4tbm8zrTVmGrepeDcf7H1cxhpWc8Gy3OoEP1EuuC5XIT2qIQgtXXpnf/n5U23c58M/fyV9Lk9hxk9Kqapf4PmhDgTB95Mi84RHzOqU+DqyzRSOiAmDNmMAl3QDX/r+7cP2/3Apf1PvoZD0NoGUIN25Gs2uBlyAqJxn1eSGEYTho2oTn7r2lbQFAqM8p4VjBOFKvps9QMUgUy0pwm+ys8cvRcmScAH48Cl8IcGfM7GQOcC2E8YShhIMoaVDCBUCqQZ7tY/39d9689vG5vymEMXnWsXHMLR5y2qPnv+705AnlfsoNj4Q4d/NY6f1HrHkEjg7l73w1lKHhNzD09Kej9ahHYfiyy9A855y+vwEAJ6+1RUrmMRYEj93wWExNS4k0AQ4yZNuydzbB/lDOn1YtbELiPKM9yHNNV6h+YO3ZePnsFLq+HMNcvwoTd34V/z0k52kfGB0B5i0bZSm+Ng9k9GOpPdThgjBVxRXFKDJNeBUrVn+mnm2PLM6EybiAtmXjhINReb3rZAbTrhozhWa4KOZCDTChwRwimJlLpjVtAYBMzQ1dZYFut/66EQ18CGZAnk5SDcJkWWKYCw2PWnC4JrWrfbD0dmGqi4TqigfUnJkSgFJMjZ5Y2mKEE9x7eAE8qwZhpNSXlsWy7yfKGzIogDAeD9DOqvo9NZ+BBmGGkfiaCRPDd7xTM4cJ46n5uE/L5/C+2fuQcQ6qQRgvfw+GhOL21T/CjzZ/rjR+Ey+fsyuO/aZ7SrVPJeBz+51PHPknhwkjIyHEsFG5O8Q5aywtrUiZ7JsMCKOYMEytLXJyZI7kaOLl5Tlbqo/rhSPoxWPoNFchUSzkhR9eiaOf+hAC9Tifuu3JKMajFhbPCSxHffziZD2WYzl+hWIpCUl3kfq/d/3vg9mcByyIyEwCJFWlYcdSAV9kCizHctzfOGGN9iAhwOZHA5BTFk7LTBgvoPjdf7kUz3zn8cYcWUdakRAAgLNXnW2Se/MDWsKGI/aXDsIs1S/pw79+Lu74qyfjHZedVvn52JisOtXauf6ewFTtEFrd1ww94xlmwkcgF7Vz2Vzltm7EJ56I4Wc+M1/JUxOBI4nh/wKBMBsHrPEjAPBkRUmOjFJivSwCu5ivkrvTccrJciHRjcfR7Plg2rw2qL7HjjUaYYwjzT0AgKGeZGdlJM35nhTBJL0ASO6915Ejsx/fE9pxyPUWEcIyYQJnm4G1a81rrhknnQ4EAOIwYQBpxn3Ik/ce5SmYlgTg3CS9GeUyMe6F4FqeoKXZCgIv/jdp9t1WhQ6uJ8yrvk6QqgrozG8g2akqbFW7DpNWjpXaHBgEJxpYs9ePGaCBI1CgJh9fnUvua1aGyKwvkCAcG8dlMswTjuROml8M8oO2mhKehz/77C040jyU26ZHgFYjwm/89IsYmtuV+0ykGXzqlwqfL7ryfdh2z+dw0ZV/CnS/iPXDq831/8atB5CpRdy8lphQYIDLhHnO3AIuudmReJnMV8BrJoyWRtAJgEv23lxZzWr0uVVjZ0aoZZwUmDCccyMZ5sWRAWGoOo+swISxi1EHmCAV1ZuHdiBlzJG0sB8Fvn2W/VQtQEXLVOIfrVjsV8VEawK0Qt/x1HXD6IXOA/aDfzIgTBUTJmQLONqUUjT3Td9n3rdyZPYc3NAsyJHxGAwCkUoC+6KQSCiwJEcdlRCBCl+HQpw8frJ5Pa7YoP2CEoqPPumjePel7678/JlnWim4XjQCAIjSKVDBIRxfq2C92k5lfQKVIMK+66VUiTqAV572GnDGDUNKS540z3O8zZYQWopppr00OTI3KbL3j/8YANBYqgd7Vg/05MLJeHFCsdv/CF785Rcj1cbJFSBMdlCCdN7QkDGIB6v/PaGSiR887VkAIYbVAwDsSJ5pJ4RAxgVO79mHaZQTTLfTXALXtLmj+84MdETeO4LLudfY2goGlkloMYz7LQuUrKyW5xTGEwY4IRWLgjBaEonAgjCde3ZWbsuYBWGCeASBEGAuCOP8BOMCVDggjFOkM7Zg55rbb7kZK9/wBmz+9H9X/qYb58Z5ydzr4xg4eGvuve0TQw5m4EhKVjFhJvrLQD9iy2psWVG+JpRQfOXcAgjjWyYM8Txs/Mi/Ye3f/k3f/eu45ATLcGHmegDf2f0dTB2R4w7zFkAGbF/z+JNW4YCTXM2ce5Qr5szX59W5oRR/ODUDocaHtbPHlxtRlIad3VtPQX+QQ3TqWWoPdSTZsXnCsEIBRvFvNywThi7KhOHC8hEEOBjRIEyNh4wGYVSRiPGEOXB35faauUcEN3PJrE/bUy0Bxplh2SR9wTNbLKT7mR3T1W3J0sSApqupZ0CbzKteY2apZoPrghh57LwG5NFzq345mhGygNluCp5qBk9hXwKmciNzxpJe0gbl1PR9Os7c91ikWXkc1WOzlgwbGhhAJ9Bz9xh00IIL3GXCqMlbgDJr7IVfeqGUr9Msm8J0jEzvKn3HRGFu9JsXbQYv9AOMM8OEYR7FgAM4ZaIBRvS6SOXECAHV82XnnPujo+Z1qOacfqaO18iRZWCEIqMWhNFPpGHhC4HUzwPLfiZzasSZE7cbsp/d98Y34ugdjsT4kUkMd1bhuMPnAALwWYgzuuXzuhxLj1+crMdyLMcvcazalAcXlsSEcUCYOkPuh1sQOFIhGoRZhAkTKVkjd5BdjuX4WUPntTk8YL2UQmBeZKr3o2a+cpB6FBsqzIVZ5JfeA4Cnb3s65gY35d4jgiOqmSBXxcwXqrV9qyLuk6iPIyUrpJgGJCWVrAY3aLOJKFHVhb6sJByI+svPHGu4Fa2/SEwY3dTW5GsRLDwKydFLyuAFgOHtmwHAVBYBFoSpZMKMxSBqERjyQbMtWcJ4sJTwqId2KOnsQ12ZqGA0Q5ADYbTMVznsPWO3jx3D6UE3+c+FlbxzEqQr17oJcAVGJIk1BIYFqCKf4sL4awCARucIuFrECQeEyShH6AUADaTRuNteLrBrUiYqVqp8FnWSKGM9H4liz2S+I9WmFoYJpTlPmOFGYECYHAiXOSCMWpzNP+JiqbutxjtTgZuxHBNGh7fxQsOcY15BimlSeSmwHgghIARI/Xy1YI/KxNSambIMkFDtK460QbaATbu/iSiZwVwT2LpiwAByE8OxkbUJ4OX245rPx0LAd3IQCz/MmxVzkyzJJzlWd+dqqlkV+0b9xtyoZcK05/ML/Mz5vh/F8KmPGzcT+Cq50CuYLwhTbWu/R0WZCbPp2n/IJVZcfyviVPkGakGcBi0HFPzZ5GlinyIxfSGRNSsmqVsGYQCYpKFrsj6uBLypy4ShwFyYLyLwWACBRn4/OgqGfWEqDJAnlsCEcYH4Zx1Xll471oidMeLgKjlmD3Zkn5P4FFfdexRX3XsUR//47fL31bHPD2zCvx74b/zLOzPs/NIIfudH78PvXPU+/Og9Mf71td/Dv772e/iXA5/FT7uXAgDohrOx6o/fKF8P5JMWVaGlmO4+XNay10FJ9fhc9H35x8vyff3gE/Kshmwyf/3qwmXMMEKRNq7DzUduxvUHFSOpwseCK2keb3y82jOmEHPf/BYAoKdYFnTBHv+OF74ot632f9rvdBYN6VaMo/PlJCCZk/vKvAykoeYdybXy9xYqGHHGS4ph1eG7sXuFSr6y6vUFV4lISgQCUKQKPK0DYRhzkq8KrJy76ic122rQFPCjQQQAMueZ/Rb9ZyyovoMJYeTIKBGgjufS6NSMeU18Hyte/So0tGFyvyiCygAwn2fBSfnUQik27JgcuAxpd856/qtz+8moZLXURVrwV0rUMB8Vx7glhOeMxW0zrsj3Jicl6Mf9OZDQjuWR7+GG0HkWenI+IIQwHjJtz3luoyE0VIKzkZTXnKLog3XTp5wPH9p1eD+W2kMdLpB6eG7xxGwRFOkHZFhPGCzOhGEOCOMyYWr23+noa69AWZ2fqMHTMxeEUXPJrB8TRoO9jsfL4L0/rd1es+JAuSlYOThf7feTpvY53+RZCca9206p3J6puYver573iKwahBGmCEU+Y1vv/Vxpm5XhXtyydwZidgQAEKb5MZBwu5boMjtnTdI2fF7uA87b81R4WdW8Qp0XNQEZGBgwcmS9cNjMkQDJhNFzbF+tyxKvxhuMC4cJU/hw04XlL+jjKjBhSnKMgiDlqWXC0PLa+SfhS9QhyWNiBOY4RE1eTM85PS77Jqo8dShPwD3PsNRlkYGerKn7kzF5rpzQbD9XSq3dnDCvp533z1x5Jp5562vx+Lt/Hb9z1fvwW9e8C/8RnlnZzuVYWvziZD2WYzl+iaOYkFsKCBM6E+OH2hDwZwldHd6eVwNDpz+dccOHPohVb/wjbPvG1x/spi3Hr1D4amJCVOJKCCEliQAAPMeE6Resxq+j6pkcmb77mECY6IQTlrxtv2gooEgDTD1O7QS7psppxW+/ulQhvm1s2wPSHh2BI0fmhw8M2+OhCF8tBA4c3ABy9NmACEueMAAw8bLnAchL5xiflIo+fiAKECZyEZOEg4YJw6Njk8fpFwuBBGEGNROGMgMcAC6YJ5z/67aX75mIeNg3KCv1hGZ4pCnAmUkae861bTQaAPTCRB6fSJKcWb3LhAmp9oSJDQgjDUk1sCgQej4QxJgduFN+rpq3b8pWika9MjOAUIqeLxMZqe8COJ46NySX+BmKA8e80/GKMYtwYc7l+IAE3vRCt32nZCkIZs+LcBP2q0+1HjKF5PaUsHJkANBLORjlxrMJAHqEYHWjOhmw/0/+BDxJsA31ia87NkmvrLM2yKq7mU6KcZUoM4l3pSnOfLugHWUMv//b9voeePtf5nX3DctAsRKUDEU3HsPdh8pJa70ghqpeZk3PyH4tzOSTTm71bdiQTBgCm0i5Z3+BuedUs+vojMRGgkRHlni5pI1LFrxyx+XmtW5X6rdM35otQY6sX8SB52jtAIITew4r5Mh28ZUQ6gLRXMK8Qo6MAJzmE9gRa4BpM+4iIl+o9oxT5ECYfsnXYhw3sri/2WIROWPtwdWSrUJ60vNjAR288MNX4YUfvgrP/4ZkiVGH2cNRo/dVEY3BEGOveAXWf+BfsO1rX110+yGVgLlu5xQWajxFTBD0TdZevZ3iBW/xMPLCFwAozyOyA9UeJ6VgLvjmaNHrJJlhwtivaDkyGoaWIcsXv59Hu/I5u/bkR9Zuk6n9NLm9t8fUvKTSS0fJAXGagUTyPmspw+buQsX2RiaPI866YEQ9m0n1udbgEoVAAG9RJoxlUFkmDK/x4mRGjozAj0cRCOATl9prMEf24TN3fUZuw60njEd4joIyNH9H5f4XDerjz48UPJgueVPuz8inCDMNvpaZMKH7uLhg7OkvzO3H58cGqCS+lClcClO6Ki7YIpmxk2oo0JX0hzQD01vIFZUEHkFCPOP5w/UYlnFQNc4/foPjRxM0gLEvAQDGe2XlhYOikLC+5sP1WoMPcjxc5ch2HF2coVNiwvQDMo6BCcO4vZulHFla+Xtm30b2UMuRye3bFebwgJUeI8KCJGla35+7wIcGYYb2V0skAoCvKHMkZGb7lf76ym1Th9U1dOQmdELlUVOT2tXzZ82Y0fOeueyuyu31PFjPN1rtQ6Utxjtr8Ndfvh1oy2T+0GyBHejK9rpMmLQNXwEIRcUHkpb7VaPcoJkwrRbmYvnMd+PxHLuUceu/QlV/Ss6olsdmXCAwUpaFe+TkX8M3Tn4d3hSVZfCLLOHhRpBbK52yU+RAGOHXF3MYEAYEsQJZ6kGYefVvE+DckSPL4AUBmO6r08xM1gwTJstMQSDzJMA/0lHsQqc/nh+wagWpo6M5+tTL0EpHcu3ZfugRtce1HIvHMgizHMvxMAha8GXo59Ogw2XCnLt6cVPDh0vo6vrEVz4TN93Yd/tg1SqM/9Zv5SiZy7EcP2uMtacBKAmyhQWAc1MNzwNau0jMCqMmi+tN0zfvyCdwhmfvRULLE1k3TvjxNeb1yt//vb7bLjV08kpXOR4hXiWrwY1g7VoErfziOvDrj/X+BHcWXq2hpYNTP+9wLQP0YrAoRwYA8YA8ptS3IIypZq/o4yklCFMJkiThkGXC1AB99yfaoex/h7tS+iwjWc73pHwcTkK4Qo4sor5d6Kr28k5HsVXk9uuGrZl53GjCUwbh2kBUMmHseKbPTeRThMQxq9eLCcbNokwQxT6JBgEFqOiStr0OCKPbkgNhYEEYDcDyJIE2AU0oyTFhhhoBLptXiX1XjswkXhlCtTgba7bwP48kht2y593/LNuRpRasckzcqW8rGWeHtsCNNvJeJ72MgVNrDA9IEGY8zLBvk2TfdQs558611yLqM+VPQsD3KIaUbNs9hxfKySW1iGaOvMsYYzg4lt/Q9bHgWZ4JE/Wm1T4iJBUyRFQt+lis+qc4MIUbC/fuybd5wV7fwAsQ0ACn77Cybivi/EmwCW3b/p++6JyyJwwfyYEwboUkc8yQdVUi90JjFM9/RibMyWuH4OrGCQ7jxVMlR0YFsUxoN2Fe4QnDqJQfdCPKmg44lW87KTFhHK1zoNK3pRgff/LH8Qdn/QGetvVpi267WFxywkpkJJ8EbUzJ8TL1geNWDZj/AGBodgcaFYkjANg3eDdOfeYKbDxlHK0RO86d/Oi1OO681SCEYPCxj81pzdfFxcdbL6xDdVXgTj+yGHNdEIL4pJP1xvlY4hhMnCz6/uaIed1WIKhNujj3mparCUPTv7WvsfORXBsdcOjbG6Sfx56ofo6umTBNB2AcUlXKU+2KSmydvCIZkr0HAAAbD8nnrRqEsXI1oRBgRCarZjutygSv9pmgBAjI4iCMBpGkJ4wGWaqvhZ7XCAIEQROhF+GAc2qoAG6bvA2AZMIQLUdG8+yx4bk7cPrN/4qTbnpb5e/UBvXx6GKCfmAi9ychBPPtsseDBmECt6iEEGDt2XIfE6eWvhMeQ2FREizOoOsXv/HIzQCAngJVRjry2bv1sExsM6+dm890Uw4OamSptHSom5R/7nFPsT9w8rOwgcp9DVUwYXD0ntyfDHjIGTA6Hq5MGG8JMspFJkw/OTJ2LEwYzkHVGMEJM0yYOqZNltm5GwAEiZzfzc5U9+N6XuDKkfUDkLjqxyjPDMCDPuxC7QnjkcRKKtbsP3H8OGIk0GweVgOcM6aLNfJyZCmfrdzeFoxpyatyv9tMhgEBECbHmyDNF76wngc9xmRdy+xL0i58ZqW/4wE7XmVZFYiXB2Hi0MeCWstkfpyTzXQlxnxf7vf8jeeBozzOMC6g5YdJWmCZEoIVZ/4GhhoV3qyF/MATTl6NjastaLtqNsZcMmdBGE/2v2NrWzj+vLxcJPdH5HEQYCBV56gWhJHnd3hhC/jl7zTFJR5P4YUBMscTRs8zDAiTpgaE6Y1I4G20MwEIq4wBAHvXXYJUrYfokD2vhw+VC0wef+ajK9u5HEuLZRBmOZbjYRBeAVWnxyhH9ttn/PYD3qYHK3QlrA5S1NhdjuV4CGLDagUCRiNIJ6cgGEOivTv6sGD8wvyWRdWJKEIINu/8ivl74sDV8HiGobh/9bDnyO5Fx/3s1cMAsHpQVrvoSmqfVbMaSjGbn5RmO3c8IO3RseNmK53kJsIe7tFwcpMLiQZhykyeuCXvjRwTRvtW1EyyNWBzcOU5JrH7QEq1zUfymg6r5EV2DHJkGkByh6eIBmahq4+te9vt0rdF3W/NhmWZNBsNeEqfWQMvIkkM6yejiUlERYGHUFU0M79hcx2cWTkdws2in+oqf9WOG/dMAwAet32VBWEcTxgQauXIAtnG3a/+bVOZnFCaSygMNwLEJqntXL/Zrjo/wpzLkWaIxCeIuvJ8LxxWi1OXCeNU3q1vTmB6WD7vR1acATc4lc+GBWE4hPANEwMAPjfUwkLvME46Xi7wvnh++f7y6rQHAQgFrIy1nISauQnUudYJC2c/t078UWlffM4uxIXxhOHYmKZmIc9pgF6lJ4wCYZSEYhAGVo7smuty26ZT0+Z13GPGE8bjKoFSTF6Y5Id9f344qgBhxqzEiOCAA4LOO923x7rmfkpC2W/zYjXlMUbgUVAniSgYMT4g1SAMNfcRZ06SpMIT5pyx7eZZ1bG+G0IokI8Un/pFmDBLqWY/e/XZeNXpr6r0vznWWD3cgC/yCd+BtpRZSj2Cb73hEvMfIBMTF17zdjz2e6/FBy98HV47cRlO3fIifPDC1+ELp74fmy4awjN+/wy8/O8uwms/+Fi89oOPxWNesr00H18sRlshVijAvc6Umrr9herIeNs+v0XvDIt2Fa7JEpgpAIyP0K1jmxGt+ZJ5/8bDN1buX2SZ2TcJAvTuqquOVuHo9SwE8qGYrWK0qMiY1KWJncNpqvv8up1liTWS6DElRfta+dxr0LMzX+UZYEH5SADgtg/addtkeXt1Gj0C3OsL4w2Wdquvnz7tbgW8qGHCeHfdrtoC+H6MAHlPGI8D39z1TQA6YajmBDR/rYUHrDh6C6Ik76+zaPiRqe42EZUrsSdGVNGRcxwGhCnOZ37rW8Af3gL45XnaYs/2n7/M7qvn3z8pMh1PPW0Nfv3CTZhXcpwnH7oIgPSSAID9UTs3Zp+7WaJfGhzPFBMmS+y4FLvnJmzBo/K59Fh5rp4U+rzfnVgJqGT5Qy9Hll9LiyX2DQ9GJMweO+PCACd1wZJ8W/vJkbEKJkzV3AGQ4KpH9HhpPWHqgAyu+xkNwmSqj6npyzIjS8iM1CerKCbR0Twq5y6EZwZUEX0uk57/UMIWBWF68/b6e2AQ0Ezx6u01o7YoRxbwOhBVMbD1OT9tprSFJ3wMCmKAZD1XW/O3f2v3opkejrJCknXhq98NQg+Peel28xnLqgAwNX93ClS0HFnmN8yxAQDdtcNKIQdyjRGGAxAVvjDy3tIAzxL91gC0LsxLla0fbeLfXnkRQlVk9KIfjOMLd37GgDCx/1sAgMl9Czjt0jyzKY2l4kUGAqKYoqJmDhIlEjBrpAP4yVXvgafOIeUpiO+DaRlRR55Nz3tFlhk5Mr5KXidP+Djx8Pk5NiQA/OCR7wQA3LZRvj8zHuC7/1cyMxlh4GC45Umfw2XPvqTPWVqOxWIZhFmO5XgYxNDKOJcMPVZPmKVUJD5cwi9UOeSSYsuxHA9RNEKVDPQiTH7hKwBjSLRealz//PHBvO8Ei+ufvTe9guARV78dJ93+Hzjpjv8AADxh0+MWbduWz30WGz76EYSbNy+67VJi82rpZdNTlaqBC8L08WTy0rzvRHZwiVIoS4yznmgZEqNrHli/mQczqrrnSiaMAmGyoGkmuUaiq2aS3W3IyuuDE+cjUfrjXqMM8NzfyGK1EFOLmpQm8J22RKrSkFckFPQ9E2d2MUazrlnoalBl7+tfDzY5Ba62bzVs5WurvRcedKWiAmFSC8Iwx9g89inemlkpAKGqvgTjFhASFoQhGgxS+51ekIuuNSMxuJZycpLS/Ogser4cj/aueRQAoH3VVeY4e9TLy5E1AlzFT1JtcSRczC5tW4YaPmZbVt7CAE5pZpJ3xFkUrm6ugte5wvzduf1283q+aUEYIQS6KUN86LG5sVSA458n1kPslj4F7Tj/XKf79lmpr4qYUV3fmmGbeCou5TUIQ9VC+Jk8xnC4uXafgJUwE4RjXZYZ41ZOAxyer1psq3tRAeERtSBMVpC/ShwpU3rCVvgkzxhaKHpHiNILfGn/5SUQZoGNIlNJHiKYZAaouHUjwT5V1U4A+CppkwQShBlr/uyM3di5LwQntpqySspQAKHK6mSpW+lZZsI8a/1jSiDMpm4EYfTW81e8mGgI3a/+nCR4vr/l07m/V6gi3mRpBBEwJ4n6QHopahD+pt0z+Lcr7sV37sh7cOjzJUBMdXR6QG6T+MDHHk9xqcPssnJh9w+E0Yk4TgjC0avM25+681OqOfn9C8ecmYRhDiCqCrcKv6eqjbtp/llxI+PyLvOcG8dT/eZ9RyokiXX7KUOwVsqjaCmWSiaMY9wcnfp8XHi7lROq8rXR1cEUwJ1ehmwRJkwOhDF9evVNF++Q8pwX3MkRBA0ESScPwrj5MS5AFQjjFYb5o+vu59pobBuazn3z4f0HgaBZ3k71sZoBLgBj9B0XvFxAPUB5/6x83R/kPvrKvV9Bv5h2fjoJjo05UxWvfNQWbFhtj2+kswqtRPa7LUZz8qp6Xqb91664fT8+c90e9NT9ndEMUezIjk2cZpgUVBTm9gduKYEwP2o0AFYPPj6YITr5+bkpkvg5RFoACtI+wAQAM77q6MuEUfeyR8iSPGE0kOHKkfE6EEYXiZA8OyRJqp89w4SBMN5Q/ZgwTPVLrhyZ6JN6JWb+41sQpubc9OZVvymYAifk9u0aSUyuASTDhJH3+WAyUdpWMGYowJoJEw1US8ef3ZOfM5KY8xcdZ6WrPdX8NLVjRpp1jByZH1KMr5Prv4R2kWb5+1qGBkrsc7+GayZylBsWydFD1hNGgzCNcTBSAcIIe7/QwbHK46sKPSa54Xk+mh3JvG03VmFzkuXGVdPubXlfltZRKU35nPVrwLRcZ80tMjQrZY0b6RBSEHhGjizFPIPxhGlfdRV8VsGEiWRfR1r2XDzmnhfjY0/KA0OCeuADAe7xZV99wwTD/JT8zpWb/xcfvvANCId/TpPAX6JYBmGWYzkeBuEHHhqOHM+SQBjPThB5v9KKh1GMPO95ZRBmmQmzHD+HaHnCTIoPXH+7ZMIoqi5t1md00r99Y+7vOiYMAOxaRdDsHMKag1eb1EMrXFyKId6+HQMXXbTodkuNgRH5m6nfggAQJ448U59qZsrzk9bW2Wc+YG0CgBUbLOtncOyB8z15sKMKt6ryhGkMBQAEBPGQKpaV9XWoPu+DLQl0Dc7tMt/x+9yPxxpbR/PSlT2/g8AtANAME/2Gm7TU4MTgBvNelyUmsavlWdj0tNpeHmsrtpWvAeHw1WJIJ3x4koAptocLwkSBhyv4KYBa7HKh9lNgwmjJsKjAhJlpy+f7kVvHTVuy1fkEkJYjmxvahCnFRNHbzgdBDoQZjHwcgUrYONW/xi6ACPM8DTcCfO80YkAHLdUmWOYwYZyxL2zhW6daA9ap6y0IMyAk+LB/RYqMC3ABZOl4To4MAA7P7oZYkFnptJDQ2////Tk8NeU/Upa6R8+zmbL1oxrsELl/0j1SDmx0Tp7nLQPrDYB3q8VT4WboSbejjl8gAzGgVM+PcUfRswUwFZU6QRhTK0eW+flEYqaT1oIj2iDvyf94LDXJhQD5ZJDNS9r3tg5vKXlf3du7EKkCj6jgQOD08YTgU5fYa6+ZvXrs2L6iLPFzrDEglHk0iJQjO3yv/LuCCXNEDENfIOYWtFTIka2MV5iklNmMt2qZMGMve1nu7zhxlNLup6fDzxIEBLdO/MD8fc71/2CYqUUZeW9kpPT9Xb6fS6JuGtr0gLfxTf9zE97xldvxio9diz2OHCJxMuwH3vInAKz8VzsEQAj+ZNIyQj7z089U7l9UJDcFY+C9Hg785V9h/gc/lG8ayazqtYTBY50EjWlrEOTl0yoA+fb115vXmWZAZhxr//7vK38vyRRDxYnNg3LM/9E9R0vbU50cpRlWv+XNAGyVeq8ShFHHSTjI1otxZMAWCvgVY7NQJ0CPd0z1xTtuPlLZfl3o74IwbJHitw2HAd9vIAQBc/pjz7mEGeeGeanHseb55wMAwvu7NNp4AZpC4F8OHMLfHzqCC7u9yuc10/YBGoRx5NXCPmtQf1VeTufSDZfWbvvH5/4x2s7ULv0ZmTAAsGm8hRe++DLz96r5TfDUuOFhFn7F3Iqr67tw23fwxk/fiB/doeTLSIoocgbEky8DWlqCsHAOZvdihlacFy3T9BAzYUTRPb7OTf4hiCLoUgeS/P/svXW4HMeZPXyqGgfuXAYxoyXbkmUZYmYr4DBveMObTXYDS3E4u9l82SS7+WWzmw07zOQYEjtmRoElW8xwdfkONFR9fxR090zPiK4kK57zPHo0d6amu7qnu7rqPe85r0JQpYRpSGTIm880CFzr0DVhqE54YtHcNEz/baL9JomJoFLPvkxad/JQkxn1tg1EQXHKohov+eE0kkGNs5IYRT6y/6qk/67e/n7dF//Cj0CRMH4dNabqZ40SJqwdx0pPPhklOkkSJs/TLdpWVqTin47rmR+J2Waq98Igmmf5QUUrYUzb0DVBTWYjG6SNwXKwij3njdhaIeDRABs4Wb1WseSSwMl2g6WQMAMDg5EdWWtP6vEdLrgfIFMUqsVithsF6oD7ybnlwvMnAQDseD1Ub4d+3a/W5XXy7mxpl2dwA0MoaCKLMh9nzelBELOQLYzLZ7ua78ZqwhjZEMHsKPFx0dBbAQAVI5qzrJ71VpyxRTwbQrtPXz9P9Qi74Yx1+DUBm0hHk4RpoolnCexMrDDxYdjPmLEJc3iKEBm588+r8RXteM2r67Ruoonjhyl5E6a0txidOh8IQ4RycUidBsqDxfMTf3I3PasvrKPwcs0TX/vEclUhcAOcmOgYQ1S4sIESxqwKTmYWzK/T8ujQ1hMFVVs6Tx0SJi3+aKcoWwyDakKv7Iosq6gmTPoYP79bZQCbmoSxMhNHwhAreV1W7CFYsb7M6RETc13cNJ50r+oIWdFv9cZpV0YL3arMYBU0zjvRNU9OfyVM7dkt3ueeH1PCRM8H16QoEgcUIpDPeaSEUX3hMfVJlqqsRllzRQZFFvXmIxVBpsrnnUeLjtGW6Ynj9KmR8JenlIAaRqKN6I/KcIwW7gXXQmiQ2qzpINBEHOIkjJXB3sJW/WcYW1SaofhugRr41xuFJcCwlYcZs/bkhMMjBNwSWXYptU3RPij6V03QAEDWiRZUBanuU6EFqyy+t/cTnwQAuLLYdcHKw5bqiW9fEd9oLCjhK4IuxEMZFzv6ZDFYN4d8Sj0tnSUqx+DWMIgpYbIYfyCqUeHLbGbCQ1iGg1mts7ClNwoutAQeSvFgDat5gRf0Xl6jhAEimxLCAvDY9XvJtEtgxn+2IKoLA9RXuB0JdpMoyFk84ID/+fNCeZUyZoegOqDOY77oisxKJLkQiu5KksgaQV4rYapJGOIkg6Umi+w4T0YVBKXeeDr/JSxcfwNaZVYoUHu9Owsje5MxOVx9tLsDJTl4nx62TohFmkJabPbgWExdEiPyxu8QijdV6yc0gLmeBzsWxF19cI14UaOEqZ1XhCMjGPjWtzD4gx9gx9uE7QnXSpjajj209yHsLx2Qm69SwhACmCZI/IBS1DfDv/p14m9iDqEcjsCRcwSaT9pfBYzDrlLidcsEll1DKbUtZEYwMwKYfSJwpZQwlWJQYwGklYmEA4aDW1aKmkBAHasjeVoNSvAWNgkLDgjiY9+W9PoISRImqfysh4pdwGO3DiPTc406HACiJoyCVx6OlDCyFpqqwzPzyWNwOJh0Ji4qlXHteLFuk64OMeYH0i41/vw2rUZzjuQ1+bL5L6vbss1tw2iW4NcvCjB8zQg4IcdMwgCAPWWefj3vwFn69aBZSdRxm9kljs2Xc5SzzTU4g2zEA+tFwkNIA/z00X588+4tYIwDlILOvwpATC2mYGXwYKa272S8cZ3H44XBH/4w8XcNKXMCUU3CHFoJU0XCHIYShhKiFYf1SJ6Qc6345YjsyOopYbiu3yRJGJk04/t1SBttR8a0qpo1OFYm+0JZoAmeeuMG4yxKQjGoVtqwOqSKr+oTIgSddDoMOS6duXdnanulbicsScJ0jtUqYYhtR0SGnK/mepO1UfbmtyT+DiHGGmfRItCWiNikUonIWIw0CStwAjH+WE5EwlBQOH6YYmcnn0exaWYWBEyqhn1E5zREVOPElFMi08mDIkkamJwD2+/T6iM6AbU3lRKmlOkBCwNwzxfJNHLOec4LZwMAnveKaPwywuj5R+WlZAVDqdunPIQRiN/9YXMyDGVlyXw8/8ypCOPsjcqhUiUDfR+eJX4XKxvgwfba6zagPsYsMTc42LkUhvsK0ceMqFNJJ1XAZCdPJQeeZyuaJEwTTTxLkGDGD0MJY8QY71NFCZM991xdFE7B7Dx8CWgTTUwYYoGFsRGRTRpKdZlp1SdhWBVpEda5V1dOWpn6vlnHwuJ4woqRSoH09NYB9QY1YapVazSl7smxIN/u4PnvOR3Pf8/pyLWeOjVhAGDxpCiYb1KSsPSKQxWcjLJNGythpi8XaoxSpltnLVkTqIThmeRCvWyNJIIWeUcSGHIGPzIvsleK+h5Nvl/xvOuxyJOB6DokjB23qMl2xGrC2OCcg3ueDmKDRgsSxzJQhAvKxXtM2YOwUG8bhGuiJEfVglq2k6uP7pwV2ZFVXe9to9E1rvqviLIAFNW3R/Rsjn2gZP+x4FTWNnD12HiMhJGEUxBGiqH4NWNlULGY9pT2w/iO5T3rDeGb94iFr5drx5gbnatsmSMEwdgWGURIuVWv/PWw7GctdsUs5loz0sJhTKxg7VL6/KJgF1DMCRuDuLqGx4MG+tyIbUzbI85Hix+ikhZ4ked+DcRittP38J3LROaob2aw/Y1v1E39orjuCGcghIAQAs8iOthx2sCO5D54LFAr0ds6WauV4vCLFdk0BI1dv+88/Z1JEsZPWnQY1Z5CRwHTkUQJAcZ2u+BjQ9H1DoDGrpsnJy3W55YlguW1ShgehnDK0xBHibXAUwk9pIqESSkC78pTxciJp2GUyqxkbsLkvfclPrPcpJ2lM3u2fr1+qvjeo66L67s7AQA7naQlyLHigrndNe8dKhucDYt7LlsBNto2zJRTWq1CSVPC7P3Yx+FtSQbEIiUM0aoPhc8+8NkaZz4VwCWWhYf3PYw/77or+kJYGwTMX3JJ9ActIj/v32DM+ri+V9jYWCIoHIS1SpiWGNFTqrb/kew/oxzEVkWfi2DyWi/X1IWR9wdlgOmAEg5DkrdpFmPqtFIQLDQ7YLHG8w/iS7szMFAdTG38XN4495V44raDwENXJzI3VHZ2JazAH9oOqlQcdZZ92aNZ310UU2wvfWVqkymyLqKam8SPxz6COnTxhMBqKOuxDReehc9dKGqdPTXwVN32R4Npw8oilKEIlpiLtWfl9SiD7DdlC/iU9S1s3iWy7UPq44u3bsEnf7cOD8vaRJE6vOrh75fwTJrdXv/T8sWJHRODfUnLw5NLwiSP3TukEqbKjqxRcXsZkDfpoZUwAeM6qM4JQyCTengdtQqL1ay7dfkqfW8P96c/H4IYCaMSHMKgwe+ux14/UkXXseMLeaj7TqzIjgx1tq/qG4GHoJkCOoelhSrqzEHkKVPkkUoMy/m10mhiWpGlV5v4vGCVES9ocyC/PfEdRsQ8jdo27KlT9PutY3IOGIv9BKyCnCfOcb7dgRmrw8poBjuHq+tgqeS16J68tjwNTCZoDcdsv1gQRCTMUkl2mC5slow1zfJ9FEOiVcOGfWykgtXbg6xMbihmukVMwfMw2jJd/67CHSGyJCPMR8dQdKyKhGkUnaehmDc8YEbnmLIAcye3J5QwKqlG2XEWRzyRAMYZzBxwcNzH2s47EQcnHJs6I5Xrwc7TAAB97UvlcQ3pz3618Vf1O9nEYaFJwjTRxLMETizbOW5Ndjg4VZQwRltbjf3YwcGmr2QTJx5GW5vOLB4aM8GDQAdKVVZOGgIWJP6uZ+c1u3U2fv6inyfe+9J1tOGC9XiBUqLmmVrtozLOq4PScVTbHU1g4rDGzKVdmLm069ANn2VwY0RdWj0YBdtVAXd53lXx8DoRl0lXnAsaVsCoFZEwuQnMOMomA86D2b1VHurJa39kbrQYVQsbw4z6Q+wcOpkiVaL3hb98ev0bqmvI2Ah5CO55sfMTqRJck4KBgkIWvtdKmFBnPnMwGJLQalF2ZJKEUVd21iQRgTQjmfW3shwVbdZ1W7RdGK25v01TkVSxRWNKcJ8QgpvzORhhVU2YMCKQeJyIs3LwTMCU2YnxTEwubdgoF+fhpcun4LMvOwtPTY1sLQjnmLYt2l5rEaAXnYs0cAJ86tX1r9mCnIuMt0VjVZolUcFth1EQ53M8E+2b5qOAuFoAMhKiKwjheJGVz4Nba22I1GLbkPeNYzj6OKtrwhTHVdY60+PyM5NjNhugGCnH6quk1IRZfNnLdYHdOJgkYSgPASdSfjmGg4fmEXgGcKAAraZUMCeAqK5WVYQeNIkICAI3f4WoLcZyOajjSZAwKXZkYAG67XWJbee9Nh20oVU1YdJIGCWkO5lKmC29tc+sL1/z1cTf3R94P8oLlmBrD/A/19Ze6wPB9pr3jgWFFLXi3pGY7UzKw/Pgd74DAMjIIdmM3WNqSClXPYMVueLMizJpR2+5RddeAgB/796kEoYlVaYbhzbW9EUpYYht4y03vwVPDz0dfZZCwhA5pu+ffwYMd5d+PzCi8zDw/e9HfQprlTDUi453sFhFhMrrlxtMq4gIOHwq1DDFkag95zw6v5QDpovTtkbjgF9OqZGg7MgogWtmsbnjido2MWTkbX4kSpiDnYJ04ONRkLNit+LSdSKr+JP3fRIVFmoljGmmz8Xyhyhynor4PNVNDyg7cl4xlhdE+lPTxN8hCUDTpF0S8VqFn35V7TMyjowhxuxxMGwc21G33dFgyoXJZ3nJGkHAWhL2qipo70nb0c1GFxgIQlmfIqABIM//3RulDVK94/HGsT1lTPQKU1Ian3icUkqYI7AjC+T1T+lhKGFCDqprwoRapT08VptoIb8AAOA0xPYZi/TzMvCcFKIXCIKYEkaNAw3syBSZzCnTySEhrU/C2NIjkFqGngtSL/16DHSyCwOxsrFxKX2NqR4vykatq3913W5z39fzfVuSEw4lMMPomVYxSwhIjPyQ83Sk3CMAwGPzkyCM7Mgs10ysEYr2KD79wCeqvq3syKJzkTMLCIm4j8diiT/Mj0gYRylyzFqS3ecEAyUGFQo36tQzOxJMniqSIUqZHqzbfhBB2cO26VdGRyGPs70vh8vIf+LsR/4d0/s9ZMuSaJSHQWjymqLZSMFMQqGEaalEpBJlPlzX0vZ3AKIfXF7TY/vEfNn2RuBYNtbvHcWIuxe/Pu0/9Vcyfh73z/gD2iVRrmIAFdoGAJgyKbJsmwhF43MdTRKmiSaeJbBiSphMy5EF3ea3T6xN0PECIaSGhKlny9NEE8cTrS98AToPikloOcgAjEVKmEOQMPcskrYRDvDIvkfqtp3fPh+/uOav9d/krA8lFGwnEkxOXnURVtmPRkoYtzO5eDcaKISeaxgqRgs0p8F5yWQUCaMyMhsrYQglyI3vSbznTKAS5oJJV+OZzuiaHbeGEx7qLdoiSmZRGQQLn1qHKV/4AjZPVSRMbLFCKSDJkQPdZ+q343Zd1cdKpXc1pxa80BMkjFyYspjPsyK6iCJhIPfLIjsyoYQR289Js31Va4aA47TJBRTvvz+yI+toS/TFiS8i5eJVW52lBE61TUv8MxYRQtWIlDCShAn8aJEcX2hZGfgmYMhFrv/ADcCan8vtisVOSQYOP3T1Alwwbxr2tUYLYsqBqQOxIC4A85Lza/ojjgtYPSvq/+deTrG8Z7n+WylhhjrU70zAS1XBYACtbjsKGQufPCDIlB2qWHKcsGFKncTwD21nJs5Htd0L51z/Thd0iQw8x8qiaAu1EjMcff8AQFgWx094iKVdS2VXiQ6+hoaNp/bGMip1EDj6nUidYGM4LrfNQiBW08ikJsayBG/+gIFPvsaAVaUWnAglTBS/kpZMXu39pIoy25UiOElmPALQxWYT861MF5a4tyf21VbuAZFG6xmePJaaSuFAVOz1JOTOEEJw88tuxt6O2p0X8kki32hpwd5P/yf+7lWLMZw//p3tytUGJHYNpthsSZSefFLbkimkGfr5VZagxUdElmrlmWcS7/u7o3pS29/6tkgJAwKQFBKiTk0YLoOccfus1Do0nlLcmbDaI4vAHz79U/16/799Tr8OGEP1GSqP++jM2TAyW/G+O96GdQcjgpBzRVQLlVvQJuYiARUBqOJIzEIwTCojYTrIeByOJzKGVUHhRP/lBWwSiqyVxwPTf1vTJo7AjEjN6jG9LmL3rBmKsfSe8z+Lbv4B9I7OxNr+tahURiMSJtuSupmjQtxxwEq3eu2WdmSh6eI35xbwjaulJSgJksG8KmTPOgv/vYrio6838ORs2tDWrycrgnb7ixNv2TXvkiT5MQX9yHKeUMJ0yPuyP7MXANA9Nh0PswVwoJIBfKiaEBv2iiClfi5VDXKhN4YN0pryHae/Q78/KG3yTjaeTSTMIZUwVUqWVMtACWVHZlKiay82VMJoEiayI7PqKDd5bO6W44G2kwKA7U/VJomo+jRxJUxjEkYq6HiAcUcq6OopYVgYJToNjcCpCGWW4aWPM4qEITwU6j9NwhipSTPqWJUSJlMWx8fBatoHflmvER1JTph71sAI4vVsODx7JPaXnDNVkTDRoyb6jf0YCaNqdm1vE+O/FTq4f1+VQkMlisRsEl0rDyZJmDD2dOFBEFPhy4Qgw8GYGSULAIAPB1tHomdHjVVxDC0veEHdzxLtIMaZwMrisXUleOUKskXxXvUwOdnaivy4eG73yXJwql4YqbperSmT9WsainNeKAtVL2EhKGdwXAdWIklUXquPrQUA7PyuWE+4lUH40uJ4Jzqwp2WT/obBTXDKsWTdtwAAFbcDgeGgyAWZNX/6TN32i5d88TDOSBON0Ix+NtHEswSbHo2CBYcbdLv71Xfj5pfdjK7MqZNJzqqyQBYuaRb3auLEg5gm9i8RvvFGUBY2QVoJU//RGPAAX76O4qN/ZeA97zp0wI1dcQ1ee831eOWqT+BNK144MZ0/CmTbxUK87HZE/ulorISZ/LfvTG5jSm+dls89zOmOsv0bKWEyWZldqpUecsLf4DvjuWjC7YNrNc1EYF7HTAyMR97OB/e9NKGEcasIJc5F8LNwzZXwZYDOqqoVcXBUeKiXMpElT9I+KXmNWbJ2C6O2IGEqFU1SeTGVjTqvVHpNqzCeUJPU1oTJj8jFBLHAQUC4qG9SXrdWB+8t24Yzb67eR2Y0WuxEJIzqe+39bWnf6FjhaqXKSVnoPz1FLIoUwYsgjPYTr9BsZVGxYoVhfR/42Vvkh+LcDEtFzKTWDEwrgzE3Ci4SztGRi2WHE2hv9Hr45Gso7jo/xKNzCV69MKrNpmrCBHHHteFhZM4Qmd2DstxD1m1H1jZ0Bn9FjSWx06D9qAlDW7YXOzpkANOwtbWHbut5+tw7ObET18zAN6PjjKthgrIiDZgmty8fL2Jbt7L9cGDEg9XyfBxWTXlZS4HyECSWIansJH2LwDdr7cjMCSBhrKpgrD9UTt5PlGD8HlGA/dWP/RaaMB3YBEhrQMpS7MioCYMkA3Ut5c5ICVNFIqZluJsp8fwTicn5yanvuykBbEIISrtOTM3BV6yYmrL/2GtdOF78v/WVr9KfPTZbEgIpgbP1B5PWTf1f+Urq/nkQ/TDepk2aOAkJTdaeUu2rXjFJqgSmGsuizvs7alVDqr1HGKxClFH9pc1fS+1fXAnj5MQ95JUCFDIWMtO+jQ1DT+L1N74+9g2V+SwVPQXx+3IuAlC/evJ30fFu2aLvD2IAcFrADcCUAcPqrHt5gAAAixFkrRzKZnQfp2Xlq9o6Pk0S62nBTgDYNel5ib/P3rEKOydF53T64GLwoAzfHweVJKhKcun7pMwCbzA3OyTiwTg7Pbi4bFG0bvzTWZNQkRnvIQ0ww228ppz86r/ChmnyHDYgo/Kyrt3B8kEUpLL3vWe+99D9Pww4FkUQIxhbyDAO8DbYZpyEsfHDvz4X3VOF+qh3dAZeYt+MeUQEPxnxoUJhSl1RT9mzeePN+vUFUy7Qr/1dDwFIV4ueSBQffeyk7bvajuxQVoyqBoxKPm2khFHEx2EpYZjQTgPAXL+sawyadbSbTNWeIiFMk+rC50A6ucJiJIyyVo/XYqsGYZIMqPjYOUe8N5xC2ANAEHhRssXF54JKFTVh6WuFUI/5IWBmNAnDiQUvjTjXda1CmN3dOlmFgKJSSR5D4Ht6zuzKcYGO9yeUMBxAxYwISEMmY9UqaFWSSEwJw3xdVF4lPTJLzLlmDJ5W03dtMZeNzl2LnUcIaYVMonlh6Pn6OWsrm1LDxK78vYltlrmDfXw71P1v2/XrkvZ8+lN1P4uj0B09R+YOXYByqaJre551zcxEW/f1UZKCJ0+ZUsJUuhcm2iYSbJh4BualEkY9jwzLRFc25lKg/n9IjE+juwTT45YHAWl3vHX83BrXRUDUOlTWyHt7V6I4Jp4hsxb04b1nvhfvOuNdiTGwiaNDk4RpoolnIRrVpIij1Wmtuyh9tmLXlAsTf7sTabXTRBNHgFynmBxxWAALEcrFpO3Uv/985gOEYMNUkrDgqYcVM9ox6BYwaucwryd/yPbHC7ajssPMRFY1qaPIAACnkCRInea9qjG1PTo3jUgYSy4wFAlzKCUMkMyU8wkSSpVjRcG1UI4VsRwP2mBVqQHEscmFk5r7B2WdYWjbVQUuZZZpHI1IGNOISJji448DiJRC3IgWGxEhpGrCyAUYY1FaGeEwZNAkH8vkF+ebC1UHCzT5ZdoOJn0qWlDl10WZfFqtQpUdWYoSwFZETTxVvD4Jc6AgM8zbxYJJ2B7KFVeChMng23v2axJmYKuoxRN6nrZX8xHijKkiI5waFuYHUd8p4zqgoN+z0jMuu+TX1sykuP1ccSxZM7I7UEqY+DLe27IF1lQRaP7dSrGfTKYdrmVgSBIP1YFdsRGlnAjRnenAb84Rv71nFzBcTBICYbkSZSXmxDVmUhM8Vt8hQcJUJOkUIxryjOHOJcr2w8G0/xdfPEfXzKFAxlW9mRA0RgzGa3oN5sViNY6JsCNzraQia2BDPkYQspogoa4Js3sN8Iu3AwDax9LsyLhWoSlk/Dwgg8DVdmQA0PHWt6Dl2mtAcyKYoezI2MQNSUeMi6denPg7oEDGydW0owQAy9a8DwDvnvO9Ce2TumfiSNQjanC+bl5O8NNde5BGtXtBHRudKvCqWovxmjCEMMw8eDreed+X8c77vizaVythJKnyxKTn4533fRkmjQJho3/8U+3+pBJrv5EkaHidALYfqwmTlwkhlVKAgmsCUv3oMx///fh/y2/IMUU+W+3totj0rD0iSPrQ1si3vrR2bVTjjhDAacGDZ0V2QUElTSEg2j/vp5uQu/VBeGYUXPQa2JeNOyb2XSoUGMywMFapbcsBbFjw2sR7hXIXvNjQYIcZBKN74HljIFIJoxIzrClS4cF01DSl/4fAjJgK8tx3pTZxshZKRNng2DCYqokWgByi3s2SriWpr6vhmlFgc8QTD57lvcvrNT8iWAaFyaN+znXvxQN8Eewq+9Pz5nTi3OWTAACt5W7clc3gUuNxAACj0bUxXJJBb01+Ja/l0qZbAABtYQgjNjfwD4vVP3yM3Xkndn3wQwiHhw/dOIb+r/33oRsdJ1QrU9JIgDh8WRNG2bCzRiRMTAlzyJowsfop55eK2o6M1rH0M0pKPc3gUGj1CRARRXGwFCUMglqlnUJXP5P7DzCZKCVK+jqKBX5Uh2VqHwxZQ4aE9UgY2T8eApaLshPZkaWRVFyrckIE/f2JWnjFYjUJU4mpScS823RYlUU1x7gd2fn6RChr0mxMRfPoNwiZBzNUNVjF8S0YFc+c2QNnYHpuXvKr6n5T9mIAMnYGjIj5V4BonLH2RbaHdmxe4JvJ50AFtlRkyXHXrL++JYeZXJPp9FEY2Sr6RIvIDI/CtwSBka0qM+CsvBq0Vczn1bxK1xusqnEXP3eciWfgrEGh/lYkDDFNrJwed6+Q35E/WdkVawqnMggir4W+Qg4WKL559kfw0NQ/YP1pf6e/3S4tSZ+e/2oAFJkWC229WbzjjHfg3We+u6ENZROHhyYJ00QTzxLMPavn0I3+AjBz6x/0a9MfS8hLm2jiRMLMiYlbSGyUnnhSB/icFH93hTO6zziifVx9Wh9+8Nfn4Ob3X4TO/MnzUFXBQUarSJgG86hMLnke3FzzXlVwY8SL3YiEkYReecHp4o2MIOIakTCLn/q2fr3TZDUkybGgNWtht8ouBkcxheRxTJpQSwEQC01p2+G4yYyxOe3/V7OfeoXEAaAlEKpPTh0Ub75V9IXKe8OMFo+a3JIFP0OZ7cbDUGcm26hoNVeee2Cy+uiWmatgEBOFjAkexuwJHBfWjBl6H6YfETeMJIPfJIWEcWN2ZOF+YW/AVf2ClGxLTwYBQhnk5GEQ2djECCdYWfSFAaiy0pKqvGAsyswMwFCIBXuX+jEShnPclIkWbnefRpC54rLUG9yNrUXXSFVTJkZuqH0wFaAFQXBwQAdqKzJb3nU7kLEMPFlZJtpVBXaBiB9hhCHrFHB+zNu6t8gTwZSgEilhLHks45k2AJE3daIuzJgKmETXjAmgZEUFcN1yRBCq3wkpZMOip76L7gOPYdTaJvuimgYgdRbnnJJaJUy9AMQRgOTEOak4bdG+dJ0icW5br7sOALDznMt0kJaDAOuFQiBNCQPOapQwFAY4FWNS2WpDNXo/9CFM/eIXwSQptXSr2FehvtPWccdnL/xs4m/PBOwUn32qyNn+19d81ul2Tmif0gIS927qj31e/7vjLsEcz0+MuPVEbJnlIoC9v6rMh7ugKnNW1YSRl/w1T79Vf9Y5HrNxkvfq+D0iS3ioUwTvLfslugkbq7VbYtKecNgcqPksDUHIYcmDyreJMccvh/i3ly4FD6LA2lefELV9uKSkuJU8EZnyEACh4FJ1oKy+Pl0ziVkG4LRgXbuhg1PlnUl7TwAgShXHGSp/3A1OGHxZj8wrpWW2q2cYA5shTn5ILeweKte09OzaGizj9iCm7o3GRSfIoIwQwfgBbQdIZdJB3P//qNE2HfjbJ4F/2AG4tYW3FYbk0GaHDgxJaIQ0AA5hm3vtzGvx0nkvxaee96mGdmRtsTFMwTXqZ5wfCcyqecVLqbjPrZS5VacMdtqhixIoRmXQlsRI6ce2D8ki8Ck3X+ijIm/i9pCBxZ45I6ofE6CE4b6PHW9/B0Z+9zvs/48js/uhzsSc16PBSDn5XPEPUwnjyMz9hnZkkkAxSKSEqUfChLH6VR3MA5VzR5On16kxx8R7bSM+HCJ++b699wMAKsXacUD1hfAQhIvPjSBtvBDIj0nihQXIjIj5ihWmzxHCIKrDYjkuIC3zKEu/F9WxEqmE2TFd2p1RoybBBUCkBGYhwLmohSfH0MHRJJEUVMq6Lxl5XdnX/VPCjowTjodnRHVPi1TW1aqO6WiX2uj8h8yL7Mik88TUrug5kw2q19mSnHKi+d+0/jvhcDE/9mN2ZLN+/C392nEjFeA0lhyrDe5gW/+o3naumvg4SvTsF4o5OzDQOjIGz04nYQBhmwpE5Ita/tTExWIqIlplL6pJGMPAi8+MJWXL8eggWY6zPnUrdky9DICwIxsYF5+5FoVJTXhmGY9MuwkfHhZJFR95k4HW4c2J/XROyTeJlwlGk4RpoolnCbKtx14U7FRA94HH9WvbH6+fNdFEE8cZRou06Mr2ov9734cv7RLclvr3YlemC79/ye8Pex+UEpw/pwsL+ibQ7/sooGqTVJMwjciA7NxZib+dbFMJoxAnLqqL2cdhy/PeedHFWPjUOnAZ0G1kR9bV/6R+XSF8QpUwedvETpPhp7kK/rdQAVJImO0DRaCaUAjKUFNGx0peB4Yl65jQaCGngmIcrMbyzpE1PkLDgf/DX8jX4p4jKUoYLhejPFYTRqtWYguSDBh8Q/Rhx7TLsTg/FUUvBGNBZEfmODDb2/V34jU9VIBfbZulBKKYzODkIPA/czpQjAUhq36mPO3R2XdMEQBBHRKGUpiGo7Mf1fnwH/ul2DQL0T02liBhbCsK8raPAbY8FWunA6NZAjObxaKnkoXY6yFr1SphtL0KAfZ/4QsYvVUQZr58ZDvZTmRsAztGhS1BGgmjXjPC4DqtOK0lClB2hBRP74uRTOWKViFZWTE2j/SI4LL6nYKYYie7U9lLRIGXzW4WJVtaK1k57Gk7O3aU6p6rDZZN2vcAlq79P3hUEFsVmSFLOWuYKGJWkTDWBChhDFdaScUKn2pSUx6CPVMQiaZtQVtKxZZ0ykM9UROGscQC3jPEfctl0VXPOvQz6tpHTq7lDgBta6Tgm+kkiOKuzeKCms8aqRePFq+ssiTLObHrpkHw4kArtDZxhVSYqLNcqLKS8neILN/bT0/2344RywBiShjxZ9mIrtO2UnfNvTr44x8lt8fa9OuB73ynps+sJO7HymFOCXzGYMqDCh+M/P57MzYsRmAHUQBZFIUW545IEsZ/ySsBAGOWUMR0jU/FuLr3KI2Ic1OQMAOWAcqVEqa+skWRlAbn+n7wSikBXkW0k0jdyqmJrQfHa5oGMfXHBa8UGd1ukEc8od0OXUwNCbLrvw+lRFPByBoSJneUCXrtMxoSMABQlME9K3S1EiagAWA2Dugb1MAnzv8EXjz3xQ3bWUbtBeKkFMk+GliU4D5HPF8Lk7IowYVt0tSxYHJHHwBha0SZgyIRffCqCmDvGCzq75M4E+qXNAnjOAUsaI/GlDp1048KlY0bo13u2d2gZS1aX/KSQzc6ThivHJkSRlkEKuv1hnZkioSJKWHq2ZH5fozo5CEcWafE4gRb+2vvVfVcbR0LMD5pmmgrE3PSSBgmj0vYkYnXZpBOCAGRxS3lAbL7VW27OjVh/MiOzHQsUC5JGJ4+p4js0kLAyYPJuaZn2NgxWKxpr+pgUR6i8x2ippGab+7cnzw3+4bG9LkptMjxqDAZZpjMvhhxRvC6rvfgoXn/BZ8LO2Bi1LMji84TZxUYyo5MzvPPXCSUNAHxMVxOkkJaIe1E40nr0DqdoBXE1P3xBDDHicbSy1mSmDKZBUIrUJMqYwLmbnjzTTB98YyyWTd8M4fhVuFDl2urHfeU1a0iYaRJAEg+mSQSJ7CsnmR9LUXCwLKQmxYlWKjEpZ1TL8NbdhnaPaB1eBN20OkAgH0jFdDYGD1NXstFF+g5kLQ3nLqwHU1MLJokTBNNPEuw7MrpyBRsnHXNjEM3PoVhx4sYcg7UPLCbaOLEYOHCPv167/aSzljJpWSsxDG9MF2/Xtaz7Ph0boKRkQEhTq2kEqaB73i8FgIQebk3AVy9OKqP0yigpxaZXikAIUTbLjQiv8wwWoBQnp7ZebSglCBjGdhqMYzKAES10ubyhdGx6UB8UAHhyp4gGViZLYMqJrN08FBdYyxFdWBRsXDaPTmyplQB57g9nirCWh325WFUv2kkZptiAPCM6Nx59nxcPdsG7evQwX27yqLLSJxrsQAJZKZuGgkzTanZCEXgUeDR70ZKmCrbmI8t+z94hiRhpKUYj9WEiRNOAGD4ZWzulQWD5UI9GBUkj8Eq6CyWdb0WALAXRufvIz9jeO/vxLnuL4hz2HIYQXWFuB1ZQRKHcUv0YG+kKOkZEh84GaGEGefiu/q3j2Xt8RgJk3Fakc1Glowhq2DNrkjN43uRD7ntim1eOuNyANGC0o/10yfqdbS/xyyKoWyU8fjktLcDt/+r7IwkJmh9IiGgYj9OKWZHFlukxjO7v3TJl5LzGQDmBMxndnkiAzGMqTvUeeGq73I/ueGDKMv3tsbJURWUjteEYRwGokBExSjKbYrfpDZ4EoEWGgdzTyb8Ot2OgrG1DaotiyYCn33JUvz+fRfgy68+EwASVlWNRvCBQvRpt8rklm9ZJDnWBgeEivC6+xluXi4aPTmT1NiRqXuQU4ZcpRVuGKthFuSie1Xenx1/9QZ9jQGAxRz4ZtJ2MrF9qYQpp5AwNC+up/yll0b9Drn+FcjBvaDSdtEIOVbtXYC3PPQ5LNx3LgCAVSqRZaO0f+x5nvhs3g4R4OosTsGorN8gSBsZpKMEkISyCk6FaQFhnrw/MpzDM8UxpSthZGAeHKalEgAMlLbtrGmpyVM2gKxM6Mn6LfDjdmSBi9MYwUjbHBBJCFhyjkYzVefdOn4K6p4O8ayzmJOwI0NhSqOvHRMyxsTUAbUMivvdADdmPZz5uvkAAKfOfd3d0oWAyDpGLI8iVyr4EC0xsrTssxhfGi+K5mFMzpPy7bPgmi7O7RR2QGucift9ePxabUBkpNWfGbv99gnrx5EirLL7SlOdJNrLebCta8LUfybHSZh4TZi0c8Bicw8bIQZldNsESSVu1Bxvy1QKb7JY15ky4aOcoiYZ84WahXAGg4m5oxvUH911rSoWwlQJNtRO7XsYU2wbrgNLKo8tP50c0OMaDwHTAZW1FkENeGl2ZJp4DuEuXiz2I8fhchWJVqlEdehsqd6H6SDjJuc7HiFoM3djZ9t6reKoTqyN+P6oT4xVapQwpiRYTG7hijW94ANbo74rm7bYmjR02kAkyRYmSJiYS0GsfauXJC+s0MYcujGaK03EnGDGeZi8bwB2ZRiEUOycElmntnTUEttEPkvMUNlCyve7kgmQiN1fvW6yn5QFgGGAEKJ/VwCYs/nXqV1sGd0OLxC/0RvPnxklM8QQGCJWd+4DH8e8Z36CTeE+kIXP3jngqYomCdNEE88S5NtdvPlzz8O5L55zsrtyXOHOjGULEiIy15po4iSga0qblleX3Q5dQC/feviLqpV9K49L3yYahsworVHCHEJerCbIAGAch8DVqYqEIqEBCaMy59bftxecc529Vm3RFceM731XLKwAbLMYzAm0IwOArJ0cc62q/ufd2qAl90u6JoxTtcjKSosAAlpT3J6nkDDjYZTlpWzPFOlAzeh6dJWFnlrGqcxUxsAMVUQ4WjxO8wOthFG49N43IUDMjkwuepwFC2SfOXr3icKV+3vOwh/PJCjLAp1BCgnjTZutXweeCWy7B75UHfgkaXewbPokOFIdFBILePpmSSBJEsZM3nsmgP2tylLCAWeAb4pzRUMPN82bh6IXBQjNuWfp1zRWiLlkA61WXmchW9Om1RxHNdKUMEwHCqq88R2ZUem2wbUMDKIF1/cf1M3CeFFopkiYEHbLFDhWAd37RT2HfBhiLFavISiV9YLYkiTMgg7xO23pkQFSO+pnMSOOi1cVlB/KJX8Hdse/yVfKjix57K0XR9YXTP6GfijtalgAGrveXdPFjS+9ETe97CZcPuNy9J+fPLfWBCh7T+8VxD6LFbxWJGLkiiSOueXJhxHIe+wBaafBwsjnnHAG9zRZ34MzGNOiegwZUpLblvV3Gvied/zVXx39AR1neHW6rZ5tlRCY2zZXvx8Wp2uCdyJhGhSnTW5FR06MZXFLGFJnub23Lfl3QZEn+p304ORtZxBskoXeT9/KawO22o6Mo700KfFRzovUaLtGhaUioSRp9QdgtGU66oEVxbVTsVMUSG95tfg/HxGuQchgqkxs5mtS1SuFmLL7NQCASza/BgYxwMaL4CqDXD6rcj2iULxbFqS0xWz4SrESROM7JQAIwTJW1mPiGAvAePL86N9Dvp8NGHxVm6aSktmustEJgREL2rMNT8MLGEbKPgJ5zkvyPiw5FbR0inFk8sg8lGO11OzQQZEQjGe6oXRQpiTYa0iY4whT2qVaMTuyLLzjmhyXsSbm+AoZCwEB1tohthfFc7beXCxrZVExRXD9e7lZKBNJwoCjq8XB5FZZpygIQdLs1YIyhuQ8rDUv7qf7D64GABTV/GwCRILDv/ylfs3D+gqL4gMP1L734IPH3oGjRMCS91caCRAHk/MCS8410+qvKCgShsaUMEC6GiaIZY44PERJqkMscFRSFCu+DPyP50TSz3CLoUmYyniKHZn0KSWcwa6Imj1mg0NVij7KAhhczu0MG36YQsIElZgdmaNJZE6sOoSTtEaTSmCVXMJJek0YxEgYo0WMzSoJqVhMzpkqpbKec1A5F4PpwjHiChuOgBDwSWfAI0SXOFQkTHbV85O7j9vUcg+mUsLYSuUSESa5saUofvESYO8a2XVFCEVExuBV/wkik0pYjKSIK2HiSYY2Ta4NTGbjeeZjUHOlRmuyI4VbFqqegZhiLs2OLCJhxN/qHJpVCZDZFSv066WtfYnPDOan1qxpH96ITjlGKUzb8ScQACUp3+vKV/VpqlCOB3Jz2dIBTNt1Bx4zR/Hnpw/U7KOJY8MpE1H5zGc+g/PPPx/ZbBZtbW2H9Z03velNIIQk/p177rnHt6NNNHEMeC74LVq9kbSeUfOwC5410cREo7Utj84BMcnbPOsF+v18AzuyalQv7p+tUPZXjJhRgByNlTAAcNqFIiNy8ry249m9Uw6tsbpBja3Ao/PLYguvhjZwZ5+Nexa4uDHjYa0VTqgdGQBkqkgYs9ouLCWQ4VeKWgnjVi0QDDcKqiiFSkTC1C58gymRekxlDaus/7hNW0MljK7fEt1/Wc4R0GQh6/zIM/Ar4zrLW9VGan/963Qb24sK4P7f1TYMGRTjaQH13kglFAYUeOYWve2w6lh7C25kR0ZshN95Fbjv6+A6qf4dOAe4WOT6VhaVERP+fd8U5yWsoGxaOH9ORGBZjosVD/8bznrk8wn1lG8CZ8QWf7N++pPa46hCoiaMq0iY9LYqAx92Fq5lYCfvxotHx/XvVPZjC3rlaEYYSKEP7pyZuq82B25dt0839UYjazI7dk1N5zZGMjJoG7PJiYr5JsfgamVEWc6rtEVX1e1kdbfp16HMrCwHMpueh6BVNWGmtUzDlLwYF5e87+8Sn9n24T876uH0SWeKvsRsS6ot7FgxCoYYOhgjSYfAA1QGLg+joC5jyL3gH/X3JjORAcmpOFbLqj/OGK21dS6eLQjqxIvVsLa/TPDG6V/S73MQ2Mdx3qnunw37RhPBIQV72Zn69WgGeOlolF3cokgYnT5c9d25Iknr4XkEVixGGFbVJVDKGG6NIesnFXEtlQ69/QNFkR3Mg6CGhBlqrZ8QNvyrXwGI7M7OKUX3/G+3SrvWWBDZZxwdnrjeKPO1rY1XCsBp9N2Qh/CLY5qoNqQVoNXWqr+r7CkrZXHMcSWMLcmDDw0O6iDm+M6d+NnTP0v0n1QpYWb1cwSSwFdFw5OI7g0zFiz844YDWP6pW3H6x2/BBZ+7HUODI6BKUUIraO2JzumGxR/Rr7uK0zHGQ3jMB4jYniXJb1JFwpCGOqpjgyK5OrxMUglTxzJpItDqTMxYYlCiCc9RWZOkUUJMzm8DAFy39n1aCeMRMd9xpCXS3Rv7I/vUuB2ZN47bs+J3yVmCZFvatXRCjiOOkZtuiv5gDYiJoeG6n50MVCthvBSSQYEzrsc1SyZ5HY4dmRlTwgBAJYW4UeoQBgYXDAGVJAyzcMeG2iAyV2FQyuCaBsaoG7MjS6mrouqwcIYRV4xhObSkkiRApLQhPIS6NENqo5xCCLEgiJEwtlapgFCwFKVQ9GyR6npTkTBGHRJGkg2MgebENWzIMfLpPf2Jpl6lqPtCZVISTAcWIjsytffAaUGZEG2lpWrCdL33PYmWcYU047U1YfIdyXHP5xlg7S/AGEuQUwrOjJUgcs7Ngmj+q885CxMxNQvJtYHJbOw1I5uuQ5Ew2bMFQWHPnNmwHRDZ5g7L5I9trY+krrXDYXEfz5ZCc0XG3L3/nkS73n/+J/3annZ24jPK/Lq2/qeti+rjTBm8EfM2/QJ3Lya4f5PYbyFjwYjXvnzdT/EPK/+hZjtbWqdgpFy/9lETR4dThoTxPA+veMUr8K53veuIvnfNNddgz549+t+NN954nHrYRBNNHA6oHT1Ey27ns9rmoom/bGRtM8rIjNnMOPZfHjFoKhKGmtABhcNY25973Wxc+ZbFuPYdE7/gPJWRsQxcOK8LBiW4dmlf3XZnXB5lyo8PRQuFRjVhAODcM/qwzg0xqzuH2V35hm2PFBmrSglTtfjIWIYu9o2Dm4H1N6LsjYHKRZxdVRPGbusBlwVuVeCYNVDCkNnn69fKXkqRMTSW6ehoJYyAttAplXTQjcWv4Rd+WQfS4gjGRrUPt1rwZZQ6AMCkvVFWacaPzjVP8bOf1xs9r0KZLqaCZGnHGhpK2WJhZHsmURPGqPodDABFS9YksVux484O+DJb0AgrCKiZCEIYpoPC2A60jm5NbMczAScWUDXa2uAsXlTTtzhUYAmIKWH0Ij85UMSz3xWht5bN0a2GK0P6cy5/NUpCwM4j67To7MuV5Y349OCHgJJoH4xHJEwmVlB1mdGGsiUX/zSrAx48UKqB5HlnVYvdzfp6rVKT6E5GAY48EwFxxqW6i4egVu11oOBmMjCCKDCRsY/dmmZuj0hUYTE7sop8PvGM9KSPLbqXblU+9bKtV9LBJcIZgn4RYBm74060Tp+ES/9qIa59x1JYSrlFpR1SA1UyydRaaZxMzP7db/Xr9tH0NosnRffqI9si2ztw2jBYe6yY3R3dS6pgdTwYlL/mav26bBP8IRepu+Z4ycAfqQruhf0iwzakBOuniW2OusDP1ydJBq2MsUaQ9cS9ZMggVEulU4+lphqnPb+GhPHsQ8/Nl20SW+qKES7DnjrXUd/9gCETykLWoQ8zUPVXAjAz+QOWxoc0yW7K69xobQMgRiJVg0EFbrnvg8lAmiNJGHfqOVoJk/VMfO2JryU7rkkYsa0zn+E6YBukkDAqAQGUICQAkcHLsTLTtnN7R8rY/NPf6GcZpx7cXDR2+E538jh5CJ/54MqOLCOfgSdQCdPRJ669TGkaKFfkUaBJ3IlGi9UCi9YfT48UealKGjkMEiaOIsR4Ns6y2DtSxlBR3Buj5SDG0seeI5tux72ShPnNpt8AAN6//P3JjTYgTQ4bsWdXZfPmus2MtmcXKV5DwjQgVeLJSEqJ1ZCE4WoOQWAZRJ+iNGWLsjXjhMHmPKGMrqQp3PScgMOxKHxqxpQwtSQM0eMcQ8mOPt/6ZH9NW9GPWDLE80TwnBl2jf0XIOxYdU08245qfSCdGNb5f3IMKxsxJYxfv64V4aGoO0UiJcxjQz+EH0b7Y0ODeg6v5z+mC4vHa8KI/Xnb7kGFEJgqGUSO2Za8X9T8isfq0zHu1yhhrLauRHfLLAcYDkLP1+fFcaN5SK6lFVTO/XiMMNX1wXjyHGSsZJ0cM7Tx1fY2qGuAHmLsmPLF/0DXu9+N6d/8RsN2AGD7I4m/M0FnajtV4+1Vd8n6QvI3XTE1KRgw8nm0XHuN/ItiR+tT+jPKfKAOCRNPzsoEYu6wo4toZefCvhb88RV/RIvVIsiXTDtet+h1uGDhtTXbenT74CFtBps4MpwyJMwnPvEJfOADH8DSpUcWCHIcB319ffpfR0fHcephE000cTggVfUE4pYFTTRxIkEI0VlPcRxJ0d5FnY2Dm88WqOA2o2YsQH7o75m2gfkr++DmJ27h/JcAQgi++5aVWP+pa/CG82bWbWc5hg7EjA1GGb+G2fjkv+/yedjwqWvxp7+7uEa5cqyI25ERIn30Y7h6SUQqMQLgV+9ExRsDlVPGTFW2P7nio6BS6q+VMMo+KYWYMGPBXhX4UySJESNhXK2ESWb6BwcO6IUWj6eKF6ZqS5k4Kr+4TRMfqgho3Ds5N74neu21AQA8WoaZEixqiwXVmEzBNyQT5NDawpWhUsJQC8wnYL4Xy/JOjjMm59jcIzLUPKcVQdFEwEVgzgg9+MRIBJkMp4DvXF47VnWNAE5VYeXCVVfVtEvsO1ZbJ261ByST8csZcf7OKIv/FaHXSwY1ITZUHtTtVXFjCwFg55BxCpr4njw2ggWVNcAaEUAOxiNFgJuJ5gWOaaMk6zUEZga8Iq61yD8/WmzHySSF10xR17NcnFefskwU0DKrLOUoq1XCxGHajg4oA0C+cOwBVEdZtMRIwEAGdsdpbQa0IzOCu4fEgXkDmxG3I/O2bgUAjMgktMXPm4zZy7phkKpCtQ2UMP7uIysSfbxhz4lUGi3l9DadeQd/e5lo5yUCduSInvFHihbX0mPXiKovEiNh7Nkz9es7lxCt1ML087FqvIjWhA1RzGYlCBAODQEAKOO6HosdAKOVgUQfWFncLyHPIOMLMqXXelr0r9Kh48uOJOBYuayzdxX8WE2pGhJDQh3Vmw+YcH1x7xWlyiUe1AsYA1VBPeZFgc5SgNBKetKXx0f0GKlsWcz2tlgLqQYLxFifUMJIu0I716uDmIxaOFBKZsHTKiVM1iPw5TNMFQ1PO1JCCGbOW64JHl71jHhw0wGEsqYYpx4IIbjiDfNStgeEnMFngd6GlZVKmGqV1nF0SHjecmGtla+0aSUMJwHAUlQAR4mvX/V1/TrgE5tNre7jP6wRqeSNaj0tenNMsSufqxUYeP7SSXjj+TMBAMMlXxOmibM+sqtmewWngHylHQ5ZLmxVB7ccw5EItFxxhX4d9qcH9gGATADZP5GI120BGteEiasDLUXCNLAjC2JKGEIIXDnnKKd8R9n/MhLC4RyvGT2oP/NSiA+ljuVSCTPsZGAFYjwqp5EwgSTVeYhxJyIktm8YrGkLAKM5RcIwWBcs0e+PjdU+tCrloh7HLMcWJIIcn9LOT3QexXHNtdS4c2gShtg2iGnq2lyeP4Zbtt2im5rbdkQKEVVH0crAZNExZ8ti/+scGyxhRyZtFatqJcVrwoQIIxJGzYOrJmYV3gKYDrxyVJ/GyUb3sGlZ2ootbrEdJ77iaLf3Jv5W++eHWRPG7OpC9/v+BtbkyQ3bPbYixNI1X0+8d8fsHzX8DgAQxqFK/p099bzaz/VzgKMce2ZSFtRVwgDAoqe+g4Mdf8ZpFaGuYRTgknA/bXIrujJduPe19+J1iyJ3AMfNYvUMsb+H5snaczuH8ar/ue+Qx9HE4eMvvsrun//8Z/T09KCtrQ0XX3wxPvOZz6Cnp6du+0qlgkolYg5HRgSb6fs+fH/iJiVNNHE0UNfgqXwtcsNErDYsgqApcTyVcapfk5Qlg7Z8aC0ozjvk8fx01U+xdmAtLp508Slx7Gr+VjYz0YSVnLq/28lC2vXus7QMuwhOzkR53MfwQRF4IpQgZCHCQ3yPQLgfNLAGPyrknCjIY1JScw3kTIJRM0APgPszGby7PIxicRREqgNMkvwOzXaCgiEEcOcSG9c8FC1sOGE12zdiBewVCaPsyIhJdXtTtlNEC+div4HnxbYf/RYEBIFRdT9zALv7EU6RC0la2x8CDiMoITQzyEsSxjfKMNBa0zaexRkGcvGmg/tmTXtfciEhtUAtjtDzNCFELZpob7ZMRtEWc86y2wEOAl/aphjMQ0AtFJzoO5wBD84neOOfEruEHQAWdRPbzr/8FTjwpS+LbfX04PMX/CM+dPeHon7G2hoQGcVpDhs3vnYWgK2Y6Ufbz1gUk8gAXD4JAEEYhjXngRMGnxFYRhY0FERCCYK0CkvDYL6PyugIAJW1Hx0nyXag6EQkzOjDDyN7zjnCosNKXmP/cdF/4B1/ekdNv32vogMu1eMea4nq1AVIBkcID8EpbTBOmqi4EfmWcexjH1OpKqpugREKyhmYsu0zAvi+nxg7lFqCgmCNbaN9/e8Bslz3P4543zYWzoAdi00bKWOBQnn9hpr3nk3Pjnp9UXzz4zuGsWr5Kty49UZ4/ZeConYcmEi0ZiyU/Qr6R4uYVLCS4+UZp6Prwx/CFx/+Au5YSvTD2X/dL2D9ax/u3r4LfxxtA5AFeHRscQu6QhHYJROHnQCYtyt5swbK2s89oO3I+uynsdtfgrzXhsDIARgHD8V58PsPRAXlJQ50nyksSwF89dGvYEX3CpzRfUaizS/PNXDJ3kW4be9b8ar+Cr6//BPg8h56+uAGTJF9L3sBpFsOKAs0cVkaq4C5HiBjSoQTFAf2gUtyyDDkuUshIoLR/fD9+QjKlah+gdUC3/dRcNpBmQjAqvE28RtwpWIU99p5DxM8PkvanJWq1vucg+iMeQqr0IHQFP2fZ0zGuUMZPOj4uCMToOizSAljePB9X1t+1fTfzKMceGDyWKlb+/wAxPPveF2rLXkRBurwXV0ThhF5/BO0z0WtUaJSKShN6LEoBUxJqpEso/4Y1tqXAaSdEvWErahpG7h6cTee3icSAHYPFsFzkQJUbYt6UfD53ae/G8P940DJxOsf/ThAgR3TWtFW2XVMx8YZw9APk8HaetsLPBGnsmbOQMe73419H/4ISCZz0sZkRbpkLANjlQClSv2YmVeJ1vwqGcn3gkScLf5dZTHGuZhXOCZF0QsxVq7A95MkqCcVI0wqYU7zKthEAhjcxOBwOWVeEqljLQoMO1mYw+JaGB2owKt4CRspw5PzS84wmDsAlY5kt6bfu4yI1KWReT3odqPxdXR4HH5PMmGkND4GLq0JDcMQJKCc5xbHyrBzSZJACa+IfJbZMqLLqAm69gn4K6ZU9SYiJwJCQCwDZijGMTt0UfSK+hjKjg1U1DHI+4A4CSXM4u0M98wFvl+Qc7Yw2Z5V1bIMw+iZe3tmFK8L1ZxcjG8kCBDXBpRZHoHThuJIlJxj2Mn5FUUIDmErp97fvWCGPA/JZ7xVZRes7NDUPnnKevho4hvb54RY9nARl9z1bgy3zIcRevjaubtTt+GcvhSVJ1cjnD0d7WMR0Wuabk17VaMxDAJcNu9i7OhX9p4eYBh1+zhp34P43cxH8LZHKwCyYBQAN5F3TLAwQNoy1ICB/3gJxcqnOe5fSFDYbWKkHODR7UPwPO85UTrhaHEk18pfNAlz7bXX4hWveAVmzJiBLVu24KMf/Sguu+wyPPLII3Cc9CyCf/3Xf8UnPvGJmvdvueUWZLPZlG800cSJx6233nqyu3DU6Nu3FwWyFSOFmeg68DhuvLGOl0QTpxRO1WsysJMZ46y8B7fdegsOJ1HWhIk/rP/DcerZxGJkiw3AwdPtMzB7h/Rh5rxp0XmUOJLrveRlARh45P4nALgAYSf1vBcHKXQxSl7bl2EP8GQgWMmlnnzyEVB+EQDggQfug/NUdWaeWFhv7pMBKG0XFtZs3xulAMQCNNB2ZGJBNDA0oNtvHQUAE4xzUCIIlRtvvBEd69aBE7Hwi1/DHWNPwyRJEoYFov9MqgoefOh+2M+Ivs+PtTPDsiBhKiKg7plljI1ZNX0/4PcDmCX2rapXynPph35N+35p3cWoBQqO4YP9YO1i6t1/8ECi/ezCpRgy7wUJKwjMLMZzfchoJUwFATGw4bH7cVA6EWwJtuBAW+1iqGwBe3fvr+mLOt7RlhZU1iRt26rbOsSIkvBjCy4vFPYJuWBMf8dWdg6yzeaNG9GvtievH04ZbvzDH9A29gxCWRD+QMeZuHP4rSisXottgzei8Ls/AO1vA2Eh7rjtDjjSpmdb0UVFK2Gy2P7+D2DzR/8F8HxBwuDQ99NNv/8tVBCi4pUT7fNjo1C5ja2DJSD2SCA8xNp167HFS99+WCkCiOoE3Xf3fVhL1zbsy6EgeBNxfTNqgYYVTVKWgyJuvPFGdD7zDLTBhVycE07w6a52vHvjU2BkBSiiTH+F+HEP+S7iKWn9/fvqnsfJ+/ehWrN8sp8d6nq+dRnB03X6smUvAWBgw74xXLTlPASbz0ToFXDfPXdh8/F0ffINAAT/9ev78OKZDMH4EABhTXnLH/+IzYUD+O054o5ZKeup3HjTLbhOfl3dcZ7n6fNMS2XMle9TDoSxOcriHcndb9+wAa0QgaiMJ5QwHeY2/flQx3mYfuCPWL3mSWyr5NC3bTtCGgXhKVcFqychP74Hrg984I8fwAdbPwgwps99V/AyLNhyERiATGBh8shccCLq7O0e3aX7/uQegul+GTBE0EhZ+K154il4GNG3XNYrYPudPwIjbwUAHDx4UG9D7ZPIgemJJx7H9p0jaH3sMXAizm2/bL9w9wGtVhnPTcJZz0RjBOccBssAJHl/qHpi69auxy7vSf0+ZR7Axf3nhQwPPPAYAEEsdBptAICVFQtr7RD7B0exwJSEKcQ4E3oEqLl7gD0si4GhYeRkoPXBxx9BuG9b4lgBoFgqH7d7LayIvgUsH2WG0wA3PrINIDsaf/kwUV03cSKP5fQWiltHKPYPjQIgKI6O1N3+/nA/AKGMy4+LO+n0zhCD6x/Alv1inPjz0/24qLwRwMpEX5dufxqQ7nzer7twQ8zCFAA2zb4Okwf+FQ8dw7FZ/f1yZhGh3rFkNm3CNABjxRI27tyJGQB80zxpY/LgsBjvhPqM4Ik1a3W9zWrE74enN64H4GLXjt248cbIfi0+vx4cEtt+9OGHUdzIwQPx921/vhPPVN1WGzdSzEUOHAz9bSthjz2qycXdqwdqzo+y7fRYiA3rVqOX2nC0nSLw25/fBDMXEdz+eBEWgJAyZChDbngzRlpnY836p7C/nCyCLiC2X/EqWLdhPSjrAqMW7rvjbmx9Jrn23L/9UXByAQDgnvvuRdxj4fffuQ/tS5JztkrZgyhfKObYXlkkt/hWHkO33Iobk+6HQgIBoe697c47MRschiTDrdDF6idXw94gxrmxgQE1Rcctf7oV1AAylQOYFFPCzJPi2KIkqSwZzN+8c4e4D8IQ8xGN1/vXbMCNZnT+1Xhz3/33wFrL0D2yGkBkVVxheWx6+E9YbROoZ+fjq9dg95Yh3YaACRKGUP3blqXtZfXa4zokYTJbzE/l+PvE6sdRCdLjUUey3tuScQEw0JCgfegZ2dH0e7OwYAH6nlyNYZOiJeb0dv/Dj2ODuT3Rtm/PHhQArFu3DoOTegGICQxlAcq+X/OcjKNjNHLdZQTg3IBNatcrCjuLOzGeIbj9DPHbXn96GR96UNxHn/neTVje1bAQ6nMaxViyzKFwUkmYj3/846mERxwPPfQQVqxYcVTbf9WrXqVfL1myBCtWrMCMGTPw+9//Hi996UtTv/OP//iP+Lu/iwptjoyMYNq0abjqqqtQaNauaOIkw/d93HrrrbjyyithNfApfzZj3113w735a9jXcxb69j2ARaseOPSXmnjW4lS/Jm/84Z8TfzOEeNELVp2czhxHPH7rDjy4cSsIMaKCiwbFqlV/ecd6PHE01/sftq3BjqFBTJ80B6vX74KTsU/qeb/XX4vHB0TWletYWLXq6sTnnHM8uDq5gJ49Zzo2PCaum0suuQjtfcmklBtu+QmKAAhJ1oShFKnH+r1bfoZStjdSwsjs4d7pM3X7p/aM4otr7otxAeJ6PbhpMx7cLLKcSfwa3jsNv13/q8R+hgOxSg9lNvRFl1yIjslidTnmuNj7/vfLz+X+x2YCADyjjM72aVi1KunNvH14O276owiUFZn6/WWtHMepOdZ/+9YXAACB6YJVgEz/AFin+N7UaZMT7ekj+8A23IURdy9a/BnYMedKzGSCcTFCD24Q4qorLkNfQSzen+x/Et+4pdafumwD8+cswqplyb6U+vowdMMNmPHhD+P0vj78yw/+RX9W3e8vP3MPIB02BnssYL14HeSHAeTRw4f0d7709N140+CH8EpyAwBg9pRerJSf/f7r4vnO1XWwbw0+c/+D6JbJYqtLL8B5lXuxatUqrP7Y54FzBfHxwmtfqC3SVj+8Gnv3iPPgmxlYjGHVqlW45We/ldvmif7Hj0s0AK658jLc8Auh5sjkson2/Nprsel7ou8r1pXwyPLoq5SFOHPZWZh7Yfr9yj0PX78tmsOsumoVCodRS6MROOf4+i13AxD3hRlWNElp5yxxD2zchME/3QYgyuYHCEYpxbQWH0/Ka7Ll+dci/Nl3o/7FjnvrM48CQ5GtxfRpk3HxqmuQhj233orxtesS753sZ8fGj4gCslt7CN5Vpy9L+kfxsy8LC42e2UvANj4DgOHKyy7F1Pbjx8J8+Zm7gVIRs2fPwqprFmBw3w789M6tAICl55+GT978Kd32gwPiRlu1ahXwmHhP8Z6WZeEqeWzh0BC2fPzjAICdXQRBusACADC5vQPjEAEXpYT5t/A6XGNuQn8wB6EhxsBFixbijKtWYe9tt+OZfSKIN5h/Bp2jwq6x5HYjP74Hn/tmiB9dOYZV168CKxaxGYKjnTKyJLHfKcPzNQlTsFr0NbL3nq3I3nEHRlxRDNqSJMzMabOxdzS6rtrKvWijg9qObPqs2Vh1rdjGI5/5PFpHDmpyeMmSxThj2WUYGhzCri0imDh16lSsWrUKxne+ghvmRkHOD/2cYP7HxHb80Mc3/3AvCJIkjLIjmzZpJi5YNVe/z4uD+NrNDwIQ3vyXXHYtfnTb1ppz/vIxBwOWq+uAmVaoj//vt7wSCzb/TaL9aOggk4usEC+58jI404UqT13bAJDNZI7bvRYGDN+47R4QEGRk7aCi24pVz3/BhO7n+h9cr19P5LFsvn0Tbt21CQMVccP0dHVg1aqzU9vuHd+Lb9xzG7rHozp98+bNwaorL0T75oP4/qZHxHvz5+PJJwWprfpa/vWvgfEtAAfMvbX1HTgxsJ0WjunYvO3bsf3z/1/ivXrbK953P3b/79fR0tqKuZdeih3/+V8wR0dx7ZVXgpyEddh/brwHKI6jrSWLkYES5s5bgFUXz05tWxzxcMOfHgAIcPqypbjzqWfQ3d2La1adljq//n+b7gWKYzj3nJU4f04n/mPD3RgaKGLFOefhrBlJC9gtv1oPPHMAnDBMP/NqbL3ncf3ZxWULq1Zdlmj/0+//DwDAcBysOGsZnvnFD7Q6BADOP/dCdE2LmJ5t94h5AicMNuWwfaHSmD1nAS66ekbNsX7t9z8GAGRzGSy+8pW46c6tYNTC4nmLcN7FSSvre/84jDVrxaB+xVVXY9cnonkMH8lj1arLE+1/fNNDciwU48y9+/6MA7uBsfxU9HpP1lw79zwgJnGEM1xx7bXY8unP6Johduhg4ZKFWDVPfOd3f/gthuT3Vq26FtQgwHg/NnzvBkgBM5j0zmqTSiVH1uOZt2gxzpH73vhP/6z335st4GL5/r/84F+0EuXSyy9GoSsDssnF6vU/R3DwZQCAcZ7H6ft+gsx1H8FvH9oPALj44osxq3eB3uY3b/0eGICQGPp4f/uTX8hPGVatemF0DncvBWKOZFZog8Y8uc86ewXaLkragB3Neu//bfrnxN/KpjftXh4FsO+nP0NnvhDZ+eaAVVc9H+1u8tree8edGHv8CSxauAhtc5bi7mc2AhA1YTL5vN5+/Nmh8Mq7mK6bE1JgdmcBf7ViMVatnFbTFgDuuucuIMrZwItfuAofelDY1XVMX4BVl6Tf201EDlqHg5NKwrz3ve/Fq1/96oZtZs6cOWH7mzRpEmbMmIFnnnmmbhvHcVJVMpZlnZIBxib+MnEqX4/UoLD9UUzb9WcAOGWPo4kkTtVrclILsDP2N6XslDyOQ8F2ZLFtakYeuJT8RR7ricCRXO/ZFjGnKI1Kmy2LntTz3paN5jiWkd6X6tK0QVjRPvq2XXvsDi2jGAJbe20UJ1Ec6JXZ+ZSnbn9X2050eL3wLUHmqJowZq6g2+czsr5MrC6CZVnCnomqukaxa7htCkCT2YL/687AdRjQtWrcrK3bt11xuV6TBbIfcw4uAwD4RgWWYdT0PeNmwFABhYN77DYs4DugC3umnMuSJTLrOLXgsRxQLusAo+2YyfZ54S8UUpFSuKfjHEzlIjvUCD10FYsoZF39naydrs72TSBvZ2r6Yp1zDgrnnJP6neq2rVkLGJT2B7H6Rb9pEQGJfWZP1A/HxJ/ZMrwSIkBBwlJse+KaKRlyH24eo61ldMfs7g+u3gbLshJe3hknCpBfPuNy3PuAyEzn1AB8H5ZlwdnrA3OE9Uij+8lktqhJQ+r/TgpGmLx+CA/huLXnUsOyMF75AnLO32Mgswct7gWwjGO/t32DwwoJfCsPxxuJrKIscawk7lGolTBUFC7ffjuAtwMQhc3jbhOWZeHgN76J4OBBZHKXx51h4Tj1xzRKayP+J/vZ8YO3zkbL6i3405n1n2PTu1qwspvhwQMUPhNKBgDIZezj2v8XnD4Z/3nbRvhMnCfTiJbbr7zpNYmCEy2MAcvfUNUfVQcrOs9xC5BtPVHQKw3hHlHnihMgI0mYEUoxz70D/WNzwKw2AACl4l4ggB6XQCuY49yLTZXzUcqIgHPfEPD+n5Zh/AvD0He/BwAoOx3I+6LO6orcj/Hw+KswZXgehmQ3bSO6nhioHuNp6MOQdWOCCkP8ZOS8VgwPPaX7knFzehvDl61C66++p88NJbLvLNSkv23L8bFnIcY6/qQEmmA06ovHKiC6eHNEwkweESqJdXftwaWviwKkPiq6HhmzXWSyrSiMbMFIIalbGKYMY6VQ14ShRqD3OdaSJDABwAlyICUR6SIshFvIw5Ttjc5OhAdlogE5fvM0ywIMmyL0GHKeIITaMh3H9d6YyG2zZOUWOFbtM1uhK9+F/fntCRImmxVj+/y+Nv1eEJv9qG2VZe1IVTMuDVvb33dMx8ZT6jp4Dz+M3Hm19SEMVbfGNGHGxoX+T34Kk//tX4+6D0cLVZ4kKz2xAl7/mjXk/IYaRK9LeJicJ8bn16F8vjly3qlqJAa89jnO1X1KGLJuK5yqOleGYYJK5QbnHJzIOkgmQc614VFX7nMvDNIHHlZdr+pASQi3wnXdqSBIn+cqq1pqGMjm2vTayxst1rRnfqTitqV1GQ0rYIaD+Sv7arevugIxJ8hkouuHBWZNexpG8ytbOvuocdgKHUwtTNXf4YxrabPtWOLZk21FZScHpMtZKM/jH/KC0HcDAoDDyLjp5yIXvT/DozBkEpObdcT7sy8A634bVlvdWLr3IuwgbTgPQOhH9nXZXD6x7d2mhZ4ACEjsvtdDepjsx4v+E3hiSP/pBBkQHs1rLMepe80eyXqvvzX5tyJX0r5vyt/BjNWDYQRozbbWzCOprBVmUAo3VrfRCH1Qs/b3jmPcBW7lWZwLDkaAH/31hejOVkulImwZSda3siwLb79oNv73zs0Y88Lj+nw41XEk5+b4VSY8DHR1dWHhwoUN/7mue+gNHSYOHjyIHTt2YNKkSRO2zSaaaOLI0PaKV5zsLjTRhIZrJQMZ1PjLlNmqIuDtdi82tE8XbzZtXU8InJxYHI0PiQCvWccj/kQhXnjdqlOMsttX8ySCcULg+0Xto0+N2gunaEhpPGxseMEQti2NSJg0ZGxxLsazYj9KieLEfLNVAdaIhJGL5yCM6hrReDSzF50xz2oAWEc6EXbldF0Aw4zOfbyYpekni0P7tKKLzMbRne0GI6LvrufCY1H9GyPlXIY0gCGLUA/0i0WP7otVdR1IG5u1PXfot8YDkQ1HmQfCRa0JBVsGNb9+dZX3NgUcozaZ6Egg9iOOv2RXaj7/Yz7KBs7aihCT/VAFsznXQYhBQ4b7TRd5L1l3pTwifofKUpFdx6q8u8+bfB5m+nPkZya48umW5yJSgkTwwkf0ayt0AG9c14ShDQqwGmGyb4SHsO3GionvXbIdPz7js/jl0i/CohOzOPVcce7Lrghy6wC5KqwRK9ijjl8FikMQaLvBlGPd//nPY+Cb30TLWLLws119PcZR5e+ePe/cOg1PHP76Hf+NzW+4CN+49tsN26m6vz9+aIc+bU518fMJhiPP5ZM7hbqEpIwlCiYHcPnH0j+M/c5cEm8B5QAhePXYWPp3AJTXCDXKyg0UGakGHCEEeUP85r4l6zGxAH7IsHNgXI9LhPgomPvEdtyuxHbZ+DiGfvYzAMB4Xpj4VXJDWJoVtqxdxamgPC+PK7pm/JChYkT1rUyphCkNjGrLQgDIVdqw0w+0RYzbEln9jb7ktepMiL7IoggsViPMUkWkl74Sv5lJo8LWhq2341WiZ0S8ZlLP+Mzq0yj67o3rYB1xXVhWFlN33lHTjgAYZKZWd7JsRHE+lLUxjF8l2s85eCYekfe1wSogsQTQzOmnxzZ8fCdq2Xax32XDfQCAuZ0LJ3wflByfcFN13bKCW3/8zZgZ7G3ZnHivkBPXal9rFGva3J+cCwBAWBH3cUulo35fvDQ7qsPH2N1317y3/c1vSW0bDg0BAPzt22G0RlHf4V/96pj6UI0/rN6Dm9bsOWS7QN6Las6masSkQdXVo5TAkL7PQUrhed1e/sZqPubI71SC2mIWgbaiYrCtLKxKlJV+gDKMlmM1aBnTtaRACVpcC56ch4KJeYBXqtqH7DsnDMseNzQJ41XSCze6nti+WfFgW1mEcmzYeuvB2r5XIhJGzbGn7rpT7I/VzqM5U/eU2LeTiz3TUkrtqiQqwkMQWyY9BUoJ42LzUHRvMHVuuR+R/1YGbjmaM3h2S2L7DpPzK9tOvK9uUh6/WbkBKucoej1kK4X6AACgzMX2g1JJHTAsKzmvHTXUgUbjS7SXqmvKTnrXOUFO9wEA6OF4kB8GfDM5XvMGw7c6V9QLoUplclJn/q62wzly7dF4xQmA2LoGKXOb/a1EkzwhjdYO9ZA2jy244ln14NbBht9t4vBxUkmYI8H27dvx+OOPY/v27QjDEI8//jgef/xxjMUmoQsXLsQvf/lLAMDY2Bg++MEP4r777sPWrVvx5z//GS984QvR1dWFl7zkJSfrMJpo4jmP7IoVcBYvOnTDJpo4AXAdC/OfjophtpUHTmJvjh8yLdGk6itnCiK0WVzvxMDNSX99ScIYEzTZP1rESRi7Tl/KiOTmIy09CEoDIGoRlxJQ3GUJMsVkNgJCEKiAN0lfnKrM+orbDkYMvRh23GhxUE3CcE3CBDroxqqCOxcUehJ/94xmwAOut2/a6cc7e8tvE3+XWQ5mynFa1IIvawfM2WfhnkwmImHM9MCu8hgfHBR9U8FO06rqi1wYrZn8mH5rXUXYQxlhBaUFSbdntVBaPTPZT1ZvEXcEKLiWXsx6LjDr6v2Yd9tN0b5JZLk1a4DhxWM2mMwqRSiDCUGg1S2hqjFkuqh0JT2T6bhYfPNwXLatvWYKpri+eFyRIVe3Jaf2dxoPb9CvWwIb8MahC9OmkIgKKiih+8YCuG6uTusIg9l98I3KxI2pjgxmyUxedW6nFwQZ1fmWN+umKpufwMBV40X4ICBKVdRgrOm65eeJv22nftvMmcmC7C2XXlan5YnD9MJ0fO2Kr2FFX2PLapUgvO1gdN3VG/cmCmrsWL1r+JBtDXB972P2JQBi+REsZpe1S1hImjK19srSob3HCxUVKAswBAsFQ5ArniPHIs7w+yf3wF/9pL7GKPF1u5KbtF565vznIdgnPitmBEFC8hVsmHwF+rOif3m2HNUIQoZAkczTfE3CVMaT91truRsbaHS/dXVHa4WcVHDqWJQMTA7/8pd6nDEtGZwKyjjLHdd1YVgsqOR5UZA9roR5ovd3Nf0GgMAvatsaAg7TcPDMZK+mXSujYIRoEoZnYhncjOHHK28HDaPvLd17MXZD3N809BIkTOfb3pral+OBvlkiiD8WCCu0luzEJb8qLOkStnUmmVgDlhUzk5Y9hUzj7Y/ZyQDi1LbJ0WtpT3jTaqmP5QTYdi8AIKgIRasiYabMb8PLPnwWrr1+Lsb5H0V7tv/oDkJi+Ne/Oey2A98VFpNsfBzWcUosXr93BO/6/qN45w2P4ul9jWu3qmFKJWR4wWGSMHIOFDZS9cn7XJMwcl5YTiFuFAnDSQjHysLiHKv7BGG6x2RYtyciZXgY6nEDpoFCxkQo/+4cEfdpaayqyDaPSBgAenwp1yFhVBJKfs1G2GZ0X1VYbWIH3RjVAFFqHUXyVCoprIoq2yhfZPLRtc9TzqcmknkIYhjIX3aJTjqxQhdfeOQL0aZD9f3YOSYENDZeelZyXmQptqFGCSDPWexZBha1ic/JN1oWyqYYn4NQkjBlX/abwahSh7ievCYCA14YJf8IVP0mdrK/TpDVaxoAoHXm78cKToD5dnvqZ3rM970ECZPaVs8tOabMb9Pvu+VBkJjSlqSQMBmPw5Cnn9F0kiWOa2bW2tJmpMrtyZ1DDb/bxOHjlCFhrr/+eixbtgwf+9jHMDY2hmXLlmHZsmV4+OGHdZsNGzZgeFhMeA3DwOrVq3Hddddh/vz5eOMb34j58+fjvvvuQ0tLS73dNNFEEycAHa97PQAkMniaaOJkIJ+xYbBoor38mftOYm+OH/pmR/caVWGMU2YGcGrDyUpLEkXCVAffTzBaD0MJk4+1qRQPIqiMRNYKKeSEI7OkzNBGSIBAut1SKz0oUu4Wi9DAcBNZynESJu9I2y652M6Oy8Vc4OvFM68KeufzSfuJVQ+7YLEMw+pzb88SljKUJRfb5dL0VCUMIKzKAFGvw91t6j6YdRZxZSqyB8tTRJBLqRosu+rcTF6O1w+PgKfs1wg97J80Pdl3Q9m1JRFSclgkzKxWcexpCzJxjURbdtsDsPuiOhYLzZfr1zO2e5gXGCjmzhRvqEBFEGiSLJRBC1guHq6aggdmFlj9MxhjB8QbKcSdKe3JWCyIpzyuVUBE4fZX3AZOfLhlaecT5MCK/Q2VMFPuvR1AuhLGsRorYb5xVW1dnmOFKQNNKnisyKeunFBTmd3dmPGDHwAQ1wYgCNAM4/BjxGQ9YhAAjCrCya6+HmNoff7zqzp4ctV8R4ILemuDdcebhDlvjiAvVNY2SchOk/e3zREFiJS1nGrJonthQNqAKeRZ/UCngmeJmy1Hh1AiDjrN7QAYAqsVntWCP67bg//+8yZ0lUcicpj4aDFEQLmcSZIw49le7JhyMdYtfAM2zhW1VUkhwF3z/xEjrUJ91hFchcBwQWKZzz7jmhjsL0RKmLHRUmL8WnjgHDhBdB1mM1HgcvHkQuIcMaliCbZtj0h2R57H0MP1BwdAmbg3tJIMgO9FShjeHZH2HkTNKLst+fv43ph+3kx74j4QSnHatlp1YJ4TGFyOZwCIGT132kMGZgCLVv9T4juuL845Zb7OTgeq1kbHOVmm0JZ8VrRkJkbNF8fnL/o8Vs1ahRuef8OhGx8BLp6ftNQpHKLvB/I7En93TY6SCf5plSD8lMUZAYBvXQv86t04eFDU02ipiGBqS4eLvtmtmD15OmqfwBECFuB7676HB/YcuvYpzabbi6Yhs3TJoRsdI3YPRffJ+r2NSRilhMnI55bXSAkjSRViEBhSNRA2IG00CUMOQwkTiHmcUsL4biuGMmIsczgwXPLjjbWNIbFMZC0TZxwQapChdpHwctt3n0psnyhFCg2xekWgx5dyqYqskeAx20PLbDwna//lndF+DEXCiO1X0kgeFa2X8x/TySE/Js21w+Q1yTkHlUoVNT0gpg1bJgjlK0mSIFLepJA/EsxKKkssFlnkKax/w9/G+hD9xoSrNgzjt96i3+83DZQskVwfMvHs8srj8jthDYk7Z68i3ShKgVLMqPu36pqyMpja8SkYgZjjuUFWq4MAgDZSAh8BPnV+stY5B3B1YUFqWzXms4qXsCNLh/yAcxBCsOpdS9HXC0zZfXeSeEkhYXJlfZmAERzSMvd1i1+Hz134OVw09SJ85bKvAADOnS0I6IxlYM9wCf9+03r8+vFdDbfTRGOc1JowR4Jvf/vb+Pa3v92wTVzqlslkcPPNNx/nXkUIwxC+nz4IN9HERMH3fZimiXK5jDAMYds2KD31IqmtL3kxjM4OZE477WR3pYnnOIhh6GAd8Jfr0GU5Me9b5SXcwCKliYmDKzPUmMxOq1FAnGAoWTkA2HVImK68I7zwOUGFENjb/giDvxFA+nXTXnAQHPBhMRsBCEJFwtQJduYnLQQ2AaGZAWsRbUMSwrWjhaptUmQsA9P3jGP7zBjhEgTgRLar6otddW59Kw/OY8Gtqv5M/cp/YfPzXwDKk4tcj6AuCePJwsuh4SBXJBjVSpj0KfXBzDNo50sw5M7AJEQBwZrrINeJQbmAah1cjeH2pVG/mQeYSQsBk6bvb/00YJl56GzmL1/6ZfzHw/+Bvz79r2s+K2RMlGPe+P2U4p0H7wIc0QfHnofxoQq+/Q/36O/097wJfN3Dei4uMk3FMXZJKyKYLoas5Fw5MF1g+33wDLWor134l8w2sc2EEkYScVWWd13ZbnSEDJY/hrLbCSfMY/9NHwIn4vq1U36nbFsXNvYBc/cmrwPCQ9hO4+DYykkr8avrfoVWZ+KSSqwqEkaRT1aM1MwuF/WLVB0byh34hGgCFGhssVFd/8ZtZJNYvbCv9gF6FqPdAS6Y24m7N4rnvGWQuvf2RGF6h7hmil4orXnq76+FMUBd18vfAGy5Qzefsq2E8QcfRG7lStgzkoWfc4yjVo+RhGeLIHOWDqOIPli0gg5zJwaC6RhunYVHth3EhhYRYFXjkkd9FCDULmPZXnAQEHAwYuKRZX+PoDoDugewLIpc9j6M2Rch77Vh3aI3oI39QrcJQqbrLzw5I8DlTwhienSYAl3Ja9QN2gFTBO2c2BjZnrMhtD/y2vOio1fjjKlUa4YDhwMG8xEAuiaY+FrMjixGcl3+WAUjk4Gh8WHsGN2BaS3TovZEWkjKzPe109LPfAamGM8AEDsa5wpWHrtQBuElLF39P1i99B0AgN6xKAkgrqIjdZ4lxwPV6tCRA6U6LY8ek/OT8bmLPjfh2yWE4GMvXIxP/FbU3Oltafzce9WSVwAPRn8rlTIAXHOasGPjseceAODx7+P7XSIAuXLHCwAA+c7a/bSQ2sDmTzb8BP/+0L8DAH593a8xu61+QWtSp5aAv3s3rMmTE+8ZbW0AgPbXvjblGxODkVL0HN54CCWMIkpUvZZGdmQqwE8NqudjYQM7smoljNtQCSPVEITBtLKY7XTCk4kVDmcYKUf3JA9DPZ+ghgXXpvDrzKk0YmqCvTNCzD0gRqTKeDpZEdW5Y3Xna3rTsXteJToZoehvuVw73nAZredSZWw5Oa20I1VyipCHMJSdsLrdDQq3IpRhGT9JqET8frrCBwACs+o5IJsSM6a0f8F1CJ8SsVi+5U5g83lC7cnEeGwEFex6/weRf/wSUNfFS0bH8f8c8XsNQmw/HB8D4ACoPYeRNoTGlDBJckoj044X4DE89ORX8MjyDyLjx5QwnCXUJMeC5b0rEDc05AS4vbhNVulLgmZkclGpqJUwdUGiWkYAMOuMbnQdBHb+2ANi80JiGDW08Ky9HEWpGGf00IpEi1pYNXsVVs1epd/ryos1VyVg+N87N+Nb92zFRfO7cd2ZUw7R8Sbq4ZQhYZ6t4Jxj7969GJL+nE00cTzBOUdfXx927NgBQggopZg1axbsag/OZzkIpWi55JKT3Y0mmgAMirahZzB782+QK+5BMfOXqZS0HEPMWDmQUZlCTRLmhMDNJhfXJ5uEaT0MOzLTovABGMyCZxKELFpkpilhDNtAAFF/IyBAIO0GSJ24bmv7LIxhFzzLRWWlD1SAgFaQtZLPstaMhY3TchDvElS2bMHQT38GvljaMVUVDLeqstmK2V5tfcBJWNN3Z84cTP/WN/GtHz2MGbGYdECA7KGUMNTBz1vyuFAFAOuoAyrmCOADPhGLShVYd53aQE5+1iXAgYeQHXs6QcLQsIJXrJiZaJvm6/zFF1OsmUkPy/ZlVuss/Nfl/5X6WWvGgjJX2W2auHL6FASxIKFhOrj7Z8/UfG+gfaHOgOd+pFjqUQVyDQuMJoP/FSsLbpXgSxKGpWQW93WIYDJLXFBKCVPb/gWzrsK9a8VS2PVzGB/fDCaVMDmndow3qYmiKwbIMeMp5EORFU1ZCOocWlU0p23OIdscCWzHkMFjWThYXudWyjWmlDAGs+ETwI9JHBvZkZlhMthafe/EQaqSfYzCqaVifsvzZmgSpqVB3YiJglLxAcDguAenAQmTOLNLXgZ0zQf59Av0W9vf8EYsWv+UDsQekMn7Wc6wLQMUGsTMNQljDGJa51xgHOg0t2EgmI5SphtTiqM470VzgF9F19qYwdACoUojxMCjZ74fC575MZ5Y+q4aAmZr+xosmD4JtmXg6lGGj8/7Nl689v3o7zoD9MCTul1YroDL8arcEaBiHgRhPjgsGOVCYps2U+qbIKHUdE0DmwqT9Zlk5WgcUeOC6UrV2pxLxbm1a5Uwnhd9z9gf1bsgXN1HVoKEqfhFAOJ6V+TI2qklzE9xg3OIjcCQFmNmzI6saz7Q/yR6hgFG1un3VaF3wquCuLFA5vHODOrfmayBkms7NivLE42XnzUVFamkeMWKqQ3bfuTsj+CfH/oCpqxejjP/JnndqblBLF9fvzI4B+EEIRHB7JZYTYY+I4NRBhgp0vInDjyhX9+y7Ra8s+2ddfs2ftddqe/v/vBHMOOGpAqOlUSgmmQm3jpOIa4aOZTNZiCJEmVHVjliO7L67YNqOzL5TCv79WrCEAAMMCx0nv5qeA8JFZID4PEdQ3jlCnFf85hdKrFMZCwDw1KR2Db4tFbDxKHi+pxwPJB3sMgXqo1gNJ2UVeRwOEfss23v/2Go720AqSVtDqxcAJSlXRhJ2pGNllLqfykCQSlhrJxI1gFQ8ZLn02d+pIRRHIVhalWIHYrriEuVBepZesVQrqrZbcpdktgcxTEpSLym4w0vB67vB2Fm4vi45wGuC5tzMFnXkTAxjgblcQCdICyAUTXfV8wFJxRlpWLW08Gqa4oQUJPDlDUaWyq5WJ0cBtKgVuCRoMPtSJAwjABBnUGc5uSaYDwiYeoqYdQPFy+tE4jr6FB2ZPP2AKtlDkdIj86K3JW/a8g4vnXPVgBAJeUebOLw0SRhjhGKgOnp6UE2m2167DdxXMEYw9jYGPJ5EazYvXs39uzZg+nTpzevvSaaOAq0Xncdhn70Y8zcLrJ1DvTNPLkdOk4ghMByDPjlEJmmEuaEwsklg34n244sXoS2nnc3lfUhnDCLUZvCR6ygfUpNDUNm09qhix+3tOCSUbnIqlN/I5O1MAbAt9tx0+QudG0BAurBqbJsaHFNjGZNdFZEhmqwV/i1ay/vqkWZaSbPdcVpQ0gjEiYNufPOwz13lDBWuhun7bsAgNBi1FfCRHZkZVCoMGoaCfOyeS/D+OPrAB/wuKzvIfvT6taSAdmeJcCBhwA2knifhh4WTE5aRrRYLVjYvhDG0AFAZq4/PFf0OagO6h0hTEoTVEicgCFcqAkO7qwNDIzlp6CFiUVusH9/ZMcRIwM+afRgKw+1PRE3Mri9chBMZXZWZzAC6G0rYBQlnU0PiOxHIIpFxJHtXghDLrYt5mCIGlA1YTJ2epBxXN4WdqVfr47iRWxPJGzbSGTwKyWMmWLvpwIvBrfhEwIvroRpUIBeWZHo7ZgNngdVJIw1ZXKdhs9OLJvWpl+PpfnrTzDMWEBnw75RLGmN7ibCq3zfT39V9JoQYFKsKLsED0Oworien5omvrx7+fW4d/SruOa39UmlSAkzhDefPxNX/+7f8AlDFBAvuZ14930/waJvLcRTiMalmUERZkytNtw2F6uXvB0VtwOE+ejZ/xgm770X7UPP4JX/aGJ57r9AOUUGDO3OBowbTyMXzkcxExV4pyPRtTYtZwqliyKjR5IKH4uLcY4jSJxHyyD4pwvfib/mWjnFAADfnklEQVQZkHZkJWnvefaSWE0Y2Z4a8Ow2UFoBODDUEt3zvh9jrQwKhOK5sHZ6gKmhsPWLD34VLyJhcOHFAIB2VltYGwAyxEYgVX+GHT1v2txo7KY8APwdgDUNTiBIo2oSJj43Y+OHrv1zLGjpSI6HSy46tTKbW1wL77z48EhwQgg++54P1v38ysW92L/hadE2dpMSiHpFSk2w8Ly+w9ofjVtD1stIOQSKMdt9BVYW1zBVpONxwEiMhGlEqgBAKIkVZUfmh/XT+hMkjHloEoZJQsCsUsKk9SlkPgAbICH8wXFY086F+bCwGrU5wb7YMXHP0+StYTlwLUMrYWZsvxlD7fPROSVJOqtrglMGm3KYMvAf7ilHBIbaPucI5HMzuOR8cQzBbvVpTd8DeXyqxktu5XLQbdJezU95Nms7VqWwz4KGYk42fSw5PvnM19eumr8Tg+r+W6ELcKASVuCaLpRzGEkhYYygjNB0UaJbEu+bavsxRU+mWl2r6tdJdbpKIOHSSYjOuxb/9PkNWL8UsAMxLmU3/BbAe0B4UJNclEEbShAkTEWO66r8YI0SBsKKzfIlRUKyMKQtGuGsZi1xtMjbSVURJxEZVw1FwvBiEas6LgZwR92aMBEJE7t25LMrTrzknvc8jNx4Y83Xe4fkc/Mol/1Zp/b8vGbl9JSWTRwuTj0fo2cRwjDUBExnZycymQxc123+a/47rv9s24brushms+ju7kaxWEQQHP9FZRNN/CUiu2xZ4u/jVZzv2QBbTqJcvZA4mb157iDT8uxSwkxqjRbuRS/92TG5SywOrNDBv3e2wY8HdlPIiUldgmCwwwwOmgaYXNxU238pXHhmVEy2a8s/AgACw0emynJLLLgVa0gQDotgXhTcT96vRlWQumK3RnU1jPrPydndOextiRaVITjMOlafFSuyI6tYUV/SbK7evOTNKFtioTvSMhMcUVZ2xq3NYmVq9cuTAXLCfWSrjo0QghuuuQGvmvIeAIBnEqhyCpUqq6kjxbLpbdEfVavClSNZGJSkOlJtmvMSVDxxLQx897v63MRtU89y2rSaCAACM4NdD92n7TXSsi9bctK2QS6UeRjqASxt0Uoo1eSEFdoYpVTLssw6ti+uTGZ12Uz9Hj1JJIzjKDuypBLGTLmfqPytDW7DB0EQI0yNBuqWahKmkXVZdV2KetY5z1bElSkhqx8gnEi0SNtHP2TIVJHL3ZlYLYsURRupCtLt/Jv3gY2L4FFZNg/nrsJ5QWPrqDgJ8+qV07GBT4dFhwGIsREAHtknarmocTIvTc56rQ16OyXZ3zOf+ApOW/8dtA9FKrh8ph2uZeCj/lvwv3v3o8AfkQcRXXvmcGRnNMfJovNg/d9g2kAbAICTAFbsWUMIgZ9tgX4eyCLcxfKoHmeMGGkTzLwUBpGZ1jGrKL8iVQQsBLEdFFS9I6WE4QZYzE6pElfOvFjUwekN0omR82b2gMnf0zCjcexDZ38YAPD7s2UNDCq+n/EFEV9NwhjtEWkT9ven7muicM6LkhZZbb2HX5vkLw2ffvESHORKdRmFoH9aaMGkEUH09M4qJOuKNQhqxokXWicQm4be6z/a8HMuVWD0BClhHtiSTjoqBIyDcqBlZxltIYGXUq9FgWk7MnJYdmSBvBfpYShhQjkmtI0ybHz5OzBy35PgpiAmsowmMvd5uRw9Vw0blkG14tOS46pXqtqHGrYox18hBy9WF6X6GBhnsTmHGBMWbpPjBjNjdVfk9/U5U/XzbNhSaeON1c5deZUSxrAjJUxo2AlLOD/0QVmypmNl02aYUglDQdFZnIIxub+Kn66EcVp9rHz4s5iz6ZcY4b9MfGYS+WCKzYMX9kWJRhwQtc/CAJRFtbAAYPBHPxKHkp2M7mFx7k1uI+QmyKhIvCIIa5QwnfK5xImBSnlIbDNM2rQl8LJvaCUMALjSho3wcMKUMNXwTYA6+dTPFAkDAM/78h0A0uezAPQ4U14XKSm5umZi66Def/yH1K/3iMc+wqM8TMug+J+/Oivx3qULeuq0buJw0AzBHANUDZjsERRTa6KJiYSyIQvDpiSwiSaOFo9f/BL9et1r33MSe3J8YWfE5LhDTpIzTm3QuImJR0tHcqF8spUwcZTqyMlb8mKRZIU2Ntl2UgmT0v1CQQQZ7UAc66FqwmTd2mvPpxVkrGSw0jFpVOgdBNxXnt/SnslIZoI6meT+Kk5bFMg26i/0Z3RmMTwaZaCHDWrCXDDzeaKN4SBTiZEwKQHvGYUZmoQR33H1CXRTbK6YXIAznlSZ+FYmUR9BgRIKatmY/cD9+NV/vUTXzQnZsc0JOnI2siQ9wEtDG7bHMbQvWsxuiwUcS9JiB4REZFncVmjvOnhmkoShY0O6IHKaEqavNSs/k/WVdq4Bh1LSpAR0CdU1T8zQweOuDaWEqUcM7uwS/zNE5IQI1J54EiYjLQOra8KYVu01aSoShtnwCKJ7lbNUawoFnRGqtpOS6ahRTcKcYha4AHD2TBHYvmbJ4WWxHysW9YlgbtlnILFI7X2vvAO/vO6XeFFuFv5vzz4gpWBzqWrsGbvtNrAREUVRJEw+m8GMsHEtUkXCZOgQXMvAObM68H1+HgBgsENY7r35RlErSdeq6hZ1Sla1/WvN9nLFyL7rrtNEH1vdPBzTwFo+E9/O/z06tXVldAzmmCBhCPPRIjOFZ235fWqfK474nQKatCMDxPNKBam4fHbtK/ZHSpjYvZ19+pcYYSJwfqD74uicjAnVIOEhyJLTUX5KFN9++d2xMSlm5+MFkdWQlRXPt18uMRM1lXwq2mRp9DwyrWhcmlIQGcMjGZnNz0S7rrKoi1mdca5qBZwImLFsdavRGPAcQG/BhQdppcqBeyXJcU6pjNaSCPj2zaq2YozZLVUhHjQ+EiVM/qKLG36ulDAkxdKUT1B94rhisF7tQIWQcZxdMZFZPYI3jzooNSBVuCw4Qg2iE5IaK2HE/4enhBF9bh0T91P/d34BbgkCKcMNlEeic8PKlcjGUJIk6/vmib5JhYZfqa4RJ39jytE7+XT07XtIf+Z7ybYhC7Ra15TqW0NZgBICr5wkVrgiTWR9QmLZsD0xbobFlPOjSBhZE880c4AkYQIzg2KsPz7zQVVNGKl4La9ZA8qise0VT34YY56Ydxp6kp88JsNmyJQPYsaOP2KelRyjIjuyiPAuZCyo+4ODAFYW8IsAVzW25HneKQq8U2rADMog8hwUWQGhTOgCD2qITGXPyqmBofVC/WHKQ0qdFy59OVouugCGJNkmDwgSZCKVMNUYd4BslY2nAklZA0wpTEttW3xQXGujsXrnPKy1I6OFpM1iNdx057zDwiULunVCS1feQSHTjCEcC549kYBTGE0bqCZOFprXXhNNHDuct78Lb7/8Q1h13b+Dzltwsrtz3KAW2FfPE4vJ9vypF0g7FVE9TjfKTj/RqGcbkc+Ja8OUBTSD2HQxTQmjSZhQLMwOULnorBPwdi2K2+f8IPGeyWy4VpoSRoIQ8IoIfKngvmUkM8y6e5LH49stCEy5WKT1iQnXNDA6Ht37AaJFfzUmt/UCECRM1ouTMOnqgIHMbv26Eive7jq199+cVhE0DGmSAOk9sAZTW+r73dNsFnCj7cULah4NCq4F2IOpnz0ZLkR2Tcxe6FWz8PtsbGUnSbPMksgmKG6LlQn8pBLGyCBf4YAOdtYGHKZ2iYxKTkXRUW/LRh3gIGmLZ0J0kNRiDr7R1qqzUutdkwMt4vc2R3+q3zPD8klRfbS3yAQbQ6q4tBKm9lhVwMjgDsYp1QQo4axGKRYHAXDOi2bpvzPZ+m2NfB7W9Mh64lRTwgDA19+wAt980wp87mW1dl/HA4o0/cWjOxEP0DqGg1anFZ9pPRPnlCuAmaKI65xb897Ad0VdiOmyWFMhl0Erqx+8BIAdPeK+yVJxL7dmLDxNZcFjw0F/5xIdPNMkjCRBs8YwLil8NbE9M6a8WT9VkTA5uPJY/2hdDNIhlBU8TsIoSy3u6ZpMk/bem9g2kcRxWZIwnhnArLKznNGZhQ7qyaBlh5GNSPkYUc1jQe/R1kX6dWVwp9gfZ7D/5v3wNm8GAGRiqlC/GD1HKn5EoltyzN6TMcF5NIYVLTEejhYVQxSmxvRKMubm8OoPnx2JdKyBjdRzBVecprK7Cd7d14N7My5m+D5aKqJWUUtX9f1afx0eJ18PWwlDKeypjS3huKwJk6aEUYq5Y0U8QSdNdRJHwBhm+ZJwAEFprH6kV1mXkYQdWf3rLlCkDVEkTH0lDJdjCJGK4nC8CItE9+++LcNR20pZjxGGJBQemH+u+JuJe7tSqSK0Yi4CNpVKFbmvoKoOSxBWdMKNZYvn5V3Lff28rhSr1C2SVCJy/mN29+g5DPfTzk/SjtW0XFi+GOfLbhdKcRImjJEwyo7Msmqu3DF/DJxzMFU3tI6FLwBclE2SBYFUDJKYYjtOoj9kzgLcVmD3o6ChGAipSiKQc8bl818CAg5LkkEjvBW+JGyQYrEbLD0TABBSC59++scA5zClEobV4Qeo7cCSapieEaWEaTxXOhaMu8AbFr8h9bO0GJ5lpq/L/R07at9MsSNrlHgDAG/qPPq1gWMa+P37LsDXXr8cv37v85oxyGNEk4RpookmmmjiOY0lU9uwo6UXnFAs7GucRXIqw5LqAyIXC/WCkU1MPKYujNmLeCc/4PL804Ud2KvOTs+6ymWVEsbB0nIFQcyLOa2WUF4qZ1SBz4q0f6lXrN42KDb0PJB4z2QWHCO5cto1VIInF90cBHv+Rdh0RJnPVRZdVm1mWdmVhZ4bKGHGvRCl2O3QSAnjZKXNkJlBtswb2pEBQMUGLJnRqCyAAMBNqU3y4rkvxsKOhaCxounzNv5MLxobYVYhCqgX7GMbxwoZC8/IwAGpWqoPEAfmniiw0TujgHEKWBURzFQ2ZUZHR2RHFlsYui//hs4cB4DAysIoxsiDlNMeVw0xamLb+z6pAyLVRePFe0T7jSsiUSlhTDudQAhUMMM/CFMFrYfXp27/eEMpYdZPFfdTlLGbPDmZs87SgRqDO/hDPoeyKjbMGdBgQU5zOUyeJ8YluzJUY+VXjSn/3+f1a2vSpAYtn51oy9q4bGFvwprseCKQwcb+MS8Zp1U3iCId0+zIyuWa9xSmHORYVi4jn3EaeJcIhKYYB3LGEABg0aQCRmgU0Nsw5zIoEZtSXZUH1ujPpzuPJbZHeXIMJZygzWmBI8f5ss8APW5GfbNKajzzkLVbUek14VSGEtuyfDFGepKoTlPCTGmLsq91UeKQpdr1kas+FX2RR89cj7iydwxmT2/UPraf0u7oHHkxOzI7I36rs0plkDgJY4vg7ui43D+vwKLJ6+xre/ejJH/qmQf/kPiMpBDPJwNh+Ozox8nE1FZxTdhhBoRTvKOvB9t7FmDOwJkAgEJXUgFAdKZ/LYbLUSLD5x/+PFYfWH3I/VvT6idbKDA5PpCUmjATpYSJB/FHy/WtXEXQHjBid1BuoH4fuKoJE7Mj44wnLADjUDyzImTVWJOmhGEyqK9IGB6EyHGG7W3CwqnbD+BLCydWKoPLe9SSyT+urWyyRJsalxE13pocTrsYpxQxXa2aCbySTvywbPkcN6DtsKpJmOj4xf/dH/hApLYLCHi1/6sicpUSxrBhBoIMDkw3YTWcVMKIcz7ps59FNcb8MewcLOk5X/W4xJ1oXulUKbuoIteq5sHF3EwAwEGDAv0bgO9eB1KlhFHPxPZOoUSypC3aRtKJgKt5aEp9mgsEaRYaDrp3GgALwFVtGpJO7I3ff79WAU/OiKQnwsPjpoQpOgSLOxcfdntyBLaFA9/5LgCgsnFj9OYhSBj/BZce9vbTMKMzh2uWTEo8i5s4OjQjME000UQTTTynMb+3Bff8w2X48wcvwQXzuk52d44bbFcGSsbF5Lxe0fQmJh52zH6rPD4xi+RjwZdedSb+8LcX4kNXpSu/TEdmNTIbq10HgVzYcLDU7CdHytKVHZkh09AMOz3gmbaNNX13wa4iBi9b2IPRnKqSHit6KhcqNSSPW20VApQyhyZhLpzXhVJs0Wbw+iSMmxOLwsDKYepBQE2lnTpB7IoVLbxHW4SaICA+7JSMN4MaeNvSt2HhjlgdgsOs7/LaRa/Fu854F756+VcP3fgQMCiBxSMLhZ++MFKH5P2Yj/XL50rLiVjgQ9uJh1GthvjCvH1WQgkDAKRMkBmR5EiKHVlHvk2/Dg0X3PNAoArN1p738Qce1OfNDhWBIwO1mfRMw5VTxIK+zcxj6vmP4fz7/gUmG0lte7zhyvupwM4GAB0sqr7eZ3z3O3A7hLLA4OLe2y6vK+Fznjw38UAODwJMmtuKs7d9Gysf/tdY8LxOn5YsQdurX4W+T3zihNolnap472VCzTJWCdKZRXVfx5Qw5XEfa+7cBTJU+4yg0grrhxdTXN8/CMvNg+ekKo9aONB5OtZOTxKMViiujQwdAgD87eXz8H9vORdl+xcAAM+ZDlPG6rQSBhWgXRC6LUb9eiSb+whcRpCxjUR2enSo0VhuFUWgksFH1m2Dsy8AAdAxEPnb6zoMluhzQFBDwoh6EOIavmHrzRgoD4CHQex5EGuf7cQS5+sAgNahp/TbwbgMYHMGqyWppPSoVBHErGwqsoYMOIPtirHkTcOjQIKEEeNERaoBCPdgVgX1FoREW8k5sn5BhNqAodHWVvPecUdTCIPnnT4TgKgNtHK7qBe0YSB6JnVMSrcWSsMfd9yW+Pv6e68/5HcmfepTNe95W7cm/uaS1Hxo6Aks/c7S5GcTRcIcphJG1dgajNfgSFVuCOiaMJQkrHnrqWGUEsaoUsJU0vokg/rKziocGESWcQy5Qj74mk33YPcnxfnlxTEwNT+RJEmmSqVe3SOixjTCkXGy2N8aFZcPqpKrgoSCTmz/9Kejca5SrPqd9NelEqajA0aotkFS6ubQxH+mYcMzRPvAcLF2/1rd0g8qer6k6p7G604pfOuJ72Co6OvjrFHCtMYKsfdvwodlrSsj5OjdJ5UwVSRMe/9don9cJWgBlIuBUKmCtLpRnnBVC2ed2YmQ1VfCzJ4qj4FQXP2UAbBQq4/qiTTY6ChMScIs7xDWnITz41YTZjjX2Iowd/75AAB7riCEcARJP5WnnwYA+Lt26fcOpU6ZM3v5YW+/ieOLJgnzHAMhpOG/N73pTSe7i0000UQTJxxT2jKY2XX4i6tTEYoIUCRAw0LMTUwohg9EyoZng++6ZVAsmlRItRYDAEv6xFth0o6M1ckuU/WGlB2ZwVVB8cZRnT/N/Z5+XTGLNRZgWduILYRrSRjbqFI1FCbX7KPkCvs90oCEac1YCGK7tlGfhFG1M0LDxlkbua7DUlcJYwFORWRKD3SIjLiQBrBTMuABsWD79XlAYWQrTL+IzoNrU9vV9IuaePeZ78aFUy88rPaHwnTrWgBAtz0bCzsW4jULXwOHtCE3uEy36Z7egtaM+g2Up7m07AjCyI4sfm5MBwFN2pUY40ZkDZJS484wKAKiis46cjcp25YYv+uuWE2YKiVMnWLG09pnAgAWrxmB5bTCrQyCH4KYOF5Q114IQd4pJYxVNWYTw4DToohPce9tsGSmP+cNLTZ4EIAQgvaxrbD9sUMqfgilmPTxj6P9Va88iiN67qFNqgkHxqusedSAppQwMTL2G39/F+74wQYMWNfVbE8V1B3JAlnlYX/t/wcA2DT7RVi99B04MPkNsfYWLC5qKWUlCUMpwfy+dtzTJ1SI3Mgg5wnSQylhlk5ZAbzp98DcKwEA7+p9GX636Ku4a8onE/3ZNJmAgMOxjESdBpKmhJE2kox4yGXakXntZQCA/FgUPFJEtSps7wdtNWPwvpEKDBnEfevvfXz50S+DB2Hs/ohd75l2WEhuEwC8YkSqVKvievu3AwCGxiPy1atEgV1L1kJyOENoR4oFZUemwSowqpQwrfNXoSj5YGtgOPFZWtb2vLvvwuR//xzm3vanms8mGnOWi2fkkosa22A9FzCntwUHcsL6Z/bBMwAAbaVIMdXaXYeArvoJi36tejVg9RUlCvaU2t9g0zXXJv5msjbet5/+fm03vGMo+hBDnHgpN6jZErCUOV6jGi/ajgx48MD90VdSFOJKZQNEz8RGShjIQu8k9mNkOYdnint++/SrMPozUVA+HB+LFNXymZmtmpvX3Jba+4vANl2MZqCJkpH+pHrR92O1pKSNoRk2UMLow4mOywijbVQrbdR8hlClLLLx5yVinA1NF1Nf82bdshKUQblKiJHzrEILqvHozscwUvYx7YAc86qUMN1ve41+zUuD6M2K+2LJttiJqkr8UHVw3KATq20bP2ppgcmUEkb8XlFyiPhfqSJ7KlkEUNurvXda8o62sWTIACyAfu7Q+tegUpaXJY9+PJUwQGMrQnexsMrUCpjjrLzOmM0EmmcLmhGY5xj27Nmj/33pS19CoVBIvPflL3850d6foIyKJppoookmTi4sqYSpKBKmqYQ5YbjoNfP167krehu0fHZAWddZTGWvSVIlRaUAxEkYF+AAZTJA3sDiaEnHIow5kV3H/vx2mA0yn3mChFE1AKoWTinERimjSJj6hFBEJAiUSf2aMKbcpwpaajKgTvuyBfQceBQAMJoXAZaQBnCMWjsyQCTLZDxg2eNfxHkPXA/HG8Yjy0+8TWLeFqR0pdgBAPinc/4JFzr/iVnlKINyyvx2rYSxZfarMSgW3fEMdSNWrBWmU6OEMcqOthQyUhbbAOCZ4juBIa5JtW1i1f7mPR/8+0RNGCPkOrvTKuRr2gOA3R8FUtmoyMR0SifHOnDSHKHoUtYaWglj1wYKLMiaMLBBmYHHXRF4Tw0sxC1NJNmlAyDHMQjxXETBjUiYwUTWszzf0vcesmhvPAg5lFmAzX3AvYuiMYWMiSBfaACm+hmz4t7c0yeyaZkbZbl6tgyyEQ/2K74S7d6wULJLcMoDAIDWklAKhvIay5/5CqB1CnD+ewEAlDB8oHI31k6rVcV4RIzRioQp+6GOgcXHayoDuiHxkHU70HPtmwAAbiUa/80qy8UQtWTpFYt79TXMQbC2fy1QibKf4zVhMPdKmFScM0ajMSIsRbWnqsd4ZY/zu6d/r9+rqFoHnGs7sszyNyW+N24nSRiCSiKACgDWRR/Ghb4YGzOjVfW2UrKXiWmi9UUvgjW5NrFgonHZGxbh2ncsxfkvr61F9FxD3jFx34xfAQBaK91w/TymDS0EAByYtrH2C6TmBQBgxKtVUdZ75vu7o7pxkPW2smefXb+TMjYTyCF7Y8wd8ngoYUoNLHSVEsaMHT9pUONFkTAbhjbgnX96J5ghnvdeuXYfYYzgUSRMo5owRJJcJGY/mA0Zsl40f9o86wUARE0YNW7YjghMR/NAtd/kb6ptugzANTMIDGhbxa17RhNtK5Uo8cqW5C2jwFhekLer79hZ1Xc1t476HieTfrH214n2qiYe1DqOGBjORnMk14u+W/ZLIDw5J6cttSSME+Tw2yd2g8v5vlul2s+dfTZaZ8lxmrGIXIj93KRqzm95Yqxzg1bsNw3sNQ2dGEPleGtPn5H4ji3te8voQeiptrW/d84xNclTJlmAhwjlfHyogYrZkgSpLK0kVdwTJwMc/M1XEn83UsIo0krftxO8LCdOcszJmtmJ3UETR40mCfMcQ19fn/7X2toKQoj+u1wuo62tDT/5yU9wySWXwHVd3HDDDfj4xz+OM888M7GdL33pS5g5c2bivW9961tYtGgRXNfFwoUL8dWvHrslRhNNNNFEExMDZUemMrCM4yS/bqIWk+e24eUfWYHnv+d0zDrj2W95p64VpYQpEmVHlr5QUXZkBjdhMEsrYRoRfesHn0Z/NlqIDmX2wa66JkMWywtMsSOzq0mesDYAUcp2y77Uz0JVAdPftPrw5uaw3gph1MlIUxYaEQmjapOkH6tnAZnSAQBAYAkCICQ+LJpem4SA4PHZBAYLtHWF79Yns4434tnojAPnVJL9zinVlLTLyDwlK4fH7cjiRImVxcxSkgixypmo7gnS7dc8Q5yL0EySMDSlDpCzaJEmYfKVNlhBjLjrqrXhAETBVv364BAAYO/c9LbHG0otR2QwpJ4SBgBMEl3XdpjRGa+Es8Oz2FDFXU+S6ucvFZNjnukb9o1FH/z49YA3DlRk0M4R98LoQJRJzUY6sXXh/8N9S8+q2W5ACagkIrJnCdKlZSwq2vvwHPGbe5YIPBLXAln6smgDhKKXBciUBKnSVhbPo1AGg8yCIHYQq/9yVbGESWGIX55HcKAAvPVvxfUYVCbDMakeA8YrgQ5sxqNJVGath9RHLtOJzIoV4tDLcRImmUmeVu4mGychCdABAzRm05O4PyjFupw4f3VJmKrnkyJOrDAaC7yKqjMR6oz2qb2ng/NYljqtYIodq/fBPZhb7kl23srgjcMjsuvJ5+gRlAE4LrBdE7OXdWsF7HMdQeWd+vWlG1+LxfsFyUnSH9mpeGTPQzXvbRjckNq29GR07VAZsJ/638kYCo+kEuCeImHE9bu3PfaMLqc/P48UceKlHIS1NUkklBLGioshGpAwXLYfC8WYWKYiIO5XaudnYWyftEoJM+7VtudKCRMbu1qsdkwZmaf/Hpa188L9z+j3LFuM1WoeqFAzBKlnqwHYZgYhjezFth0YTzTVNoaI1NOcREreXRuGqvqudpqe6PSnWx+uekcpJ5Tyw0BAJFFvJgnssl/SdmSGJMyN1lrrXifIYKTsRwkx1apkpwXUUopnHllfxU5UtR2ZLa/bjsp83JHNwCcETiAV83K+bqt4ovy9Xflc8IJOsO0iSSFfqv29HZPq5wblrrQjE30v1FHtA4AZiN+qXJaqLM6SCSrHiNbOPv2acCBIsVLTn8saLkxaDNKUWpHHgrYq5bJV7R7QxElDMwIzweCco+gFJ/xfvYfj0eAjH/kI3ve+9+Gpp57C1VdffVjf+frXv45//ud/xmc+8xk89dRT+OxnP4uPfvSj+M53vjNh/WqiiSaaaOLoYVUVJG4qYU4semcVMHNp1yE9e58NUKopizkgnKColTDpcw3LMfRCzA5dUFkTplEC2LKeZfDNCn512pfwq9O+hEppRk1QbOnUKIORp9iROdUqiLbpqEYgM7/MBmsPlQG5gQQYWZAHI6jpi4IpSZhQkzByYV4niP325e+GWVXXJaR+XYuCnmwPHp+d3NZ4a7p12fHEnCni3JdGomCjKjYeByEEs7pyUU0YubSI25EZVuzkUwNmcX5iG0bQotvWK1Kt1DOREkYV404pbG5a+vO2ci8ogw4sOE76hcBfek30WqVIHqLI6fGCso40YIARGilhrNprjAIwZCDICUQxaUAGFqiBzre/PWqctlZQwb3jbIPxXINBCVbOEoTGWCUih/nWu4AH/1cQMQBg5zC4dxy3fKPWdnDZ/jfXvMdoRFAQ08SiH38M7ZM36c9DexoAwLPF/Uvdqt/VdHGWV0amLEiYJTu65HalRYwiMqqulbcMjeCHlxh4z3tMjGalXV5xMQghWg3HeOTYQ2LjtSGLTgfUQzYXPQMVOQ0AFaet5jirIUiYKEu9c9da0EqMhLGSXzqYnym3FY0RrKjGsxAmpZj9+9/pz1SNgiVtp+v3fE8Fdrm2O+uafDZ8/6ZoR4Tj/ML/6j8592vGfGTaYeeioOYzHXdE+21gndPEiYdtR9niM4ZO068vvaCWFK03m/uHe/7psPeXUA/IZ46RTyYqxBUuPBBBXaWE+daV0XW///Ofb7iv4ODBw7IsK8fITc7r2H8hpoSJDReUA36Y3l7VhOGy3ohPxX3iH0IJY2oSRhzr/ZsHajcuFTBxEqZ7wMDa3rv132o+yIoRaaLmka0ZC7cuyYHIsY/UhEnls9UkIKaNOXsj9U25nAy0J+zIJDFx8+kGWoeEmsrJVSXWMHUlpZ+3zmK1TZ2qiSf/JBSLpPrKswqJ+XLZr+hjURaMhlTC0Ng4lfGzuHHtFp30QVBNwuSjrbJQj/HxJwWxkvOrDhYlIPw614qAEBQq4pmjx38eETtAZEcWBlFyDkmx8iOEwJR2cJMHHYAzPf9pyfTVtFcwVV2euB3ZBMZRT+uMxgwCoNPtrN9YPlOCfftE+zp2uUeChWsiUpfEFM5Pznz2rz2fSzh5qXV/oSj5IRZff/MJ3++6T16NbJ0CuEeK97///XjpS196RN/51Kc+hS984Qv6e7NmzcK6devwP//zP3jjG984If1qookmmmji6FFdi6RZE6aJeohfK06QxdqW6ViB+iQMoQS2a8IrBbDDDAxpfdCRq68k+PuzP4hX/+7V2FvYAgCgQb7GHsYxjSgjOqGEEdu3qxZ8yHVhgXs7NpQvxQi5HQV+qf7IVhneKcjHlCaDsoZD3ZowWgkjiSm5uK0Xw373me/GQ+HPE+8FtH5m3JKuJQAhKNlARq7jA/fEZ69Nnd2K/diNHkbBGQehBEEsM/LKtyzWrx2TYmcfkPWBUJK9PIgVzK7K7iu33wZ38DL9N+Gt+jetd435hrJjEtvSRXVTPK6JaSSsjqzQipQ2TvpcudDRh4MZoFACILMS0aCmyvGEIkEBIDRc3Xe72n4PAAiBGZQQmhmZYSqVWTwEKEFmuajh4y5ZkrqvcFjWpzjZ6fh/gVDk7lN7xpC4SssjgCwOzM0cfvDxBw57mwEF2rxYoM7KIBsL3PmZxQC2oeKITGeaqfpd7Sx622foeiw2F/Y4moRRRMZkWfvJsIGXfQPX/eTN+EyViNNoEcSRaxmwTQovYPByIohkewScMRBKdUa1b/jIZiM7zlxxj34d0iSZylOSFTKWgdBQZC8BHwdoEB1ftVLMkeMmozZw22eAS/5RKwU4OAxKYM+ZEx2PtMexWDTeBp4KfodRAkXHHITxulac4/UzKK5Tjm3Egrnk5cnOuwXg3TcBv36T+Iq/HsDFAABKT47tYRPpeMVZU/HbrU/hilIyIHrheSlFrUmtXWojlIMy3CqlgtkV3VjbSwAq45hVXaMyFiRWhIyqE6VIUQAoPvhg3X3vuf5jGPrJT+AuXoxZv/h53XYAsGuolPi74jNtOxhHIEl8K14Digu7MCtFickkOcOk4kMlV3g1NU+SJAyV9157zpb/W3jq4FOY2zZXZ/er+iAJOzJm4Om+h3D+tpeI/Vk5cM4RxEgiR6qPChkTP5n+aly84cc1fRGQx2hSwLDh+ADl4rcYqar9VZG1pMAZTEmy3beQ4NJf34fhtrlom1Jli5pSEwYAnPIAKm4HdrauT7yvk1bUmMdCWBADEKcGKk4rDhQPoDvbDc8vg0AmI8kkFEWWGKEHJudUreNZTKnsiyXEVP0mdi5iHZ2CTiSKqxarlTD5SkS0m8yGD8ANRF9sSbaU1z2F1he+ULdTNVtYmNXzbMrTx0hDKmGyfgb+Ez+I5pFuuoVvzz98BDu/ey8AwJfTcAIOs68+aXMs6A7Chol3lfVJdRzNpNuFdb33vej/yldSP6tG4jeI7dtvRv2fVWjOuJuowQopEz9cHDhwADt27MBb3/pW5PN5/e/Tn/40Nm3adOgNNNFEE000cdxRrXwxmkqYJurAiAWzusemYwsTgdo0ixgFOyOVBoELKkmYrnz9DLAuNxnRo+ZYzWIlE8t8jgc5VADeTakHcnnvd/C2ntfhzoVPJd63GhAZBiXa6mbtbpFNWK8mTGRHJvZ9KCUMAG2NpfvSoFaOQiYe4zuM9hONBXM7wMFhc4LhIbHQ5ZUoKDJnWY9+7ZgUvimDByqjszKuz03WSfqPn9O5JfH3wc4lYDJjb29X+mJ4WpsIllbbkVlW7aKVmCZaRrfpv10/F9mR1UlYKjgFnVm8eoe0/jhJShjDpFpRsGaGi0Aes51mS0epDlrU2pEZ0T3FeU22ZzAYEVXUnVgbjCaANknCfPf+rckP7v4PYK/IVn300SNTuU0OAzw6533RG4YNEhuYQ1kXppQR46udrx2XXrDg5WgZFUXouSXUg0yqHTUJk+0APrgR+PAWYPGL8E37r3BmldWREUSEirLyKbUKuomD4uD//A8AgMpgqmd4yObFd/at8sSILrOg24eSwSiSMj9xLQOmK8cZQoAKBYnlk5rVJIy0yQwNB7jz34FnbgZ0xnpUE6b99a+X/RQB1cJTkb1bKBUBJH7vGCZOj9X5sQJgT+xn5DQHu3thTf/55CiIf/7aKBBvGbVF3Js4eXjLBbNgzUnaNf3wzE9PiIr5vX96L27ddmvqZ6S7B5f+x5249P/7M7YfrLomUkiY4AgeT97WrRj6yU8AAOV16xq2Tau3Ukp5D4iIkvjsygSp217VhFEkjCeTKw5XCTOpVdw3XvZ2vPJ3r8Q/3/PPuo1SwMSVMPYo0fsAAN9uATbcgrIiVzlDRlqatmYscK+RXbBK/KC6/iCVCo21O4cTLX1ZS4ryUFs/z8x167lgDekkD5VU2ZF1SAs7k1U/n6tJGB9rewlMXyhPQsPF7nFRayg4sA7gSpWcnEPE56YOy8JhZU18pFqjzbtK/D//6lQ1d3UNkvg8+qpRX9qRSUJIJiIMfOtbooFWwoj3wzCv69OkKWGASNUSmBn4t14fWeAa6fO8zOmna9tJJS6jtnXcHArOmHJuw8/H77478Td105UwrS94vvg8pZZPKuTx5C+6UL915rRzDu+7TZwQNDmxCUbGMrDuk4dn4TXR+50o5HLJ7AtKaY3dmR+TxTKZBfH1r38d55yTvMGNk7SAbaKJJppoIolqEqZpR9ZEPcQXJIyEWuJfT6UAiIDXGCpCCSPtyIwGaqvqbNA0LJ3SmmrOoALqbkpAnbz3ITi7H4N3azJrzD6EWrgoPdCHS2J+Q+ssykw532LKW/kwSJjdk5Of8SPNfJ7AOd7hojVnY5wAeQ7s319EW0cGVAYOiGtEwVoAY5Ugsg9iHPzer4Dv3QxORFKPm2lLbHu2W8ATsb/7O5egb5/I4PWt9Ouip60Le7YPIJBZm1oNlWJHBlOUCba8Efh2AZkgG7P0Sj+XNrV1pqCrprgncQ5LLApUGG684u04b4O83nMpRCIhOhvUCTIILVWcmAl5lpRoca+2VgAvRdnO5qRJNZ83cWx49cpp+OkjO6veTY4F9/85GVx6+T+swO57H8C9dxrY1VJrUVZAiJ2L3ga92upeAI4/689DezK2Tr8KpYyoheUUUsiM065Ay9j/CQKEtsGzWmL3R2zMznfrl4/nnofPHvguVk2LbHHy5av069aMif6xCkraK41g+J570fWud8GQjw3P9GFagqShV74EuPH3OO+Bj+Ng5xJM2nMvtk+7IlJkpYyn0zqyGI49g17ySx+cKI0RqxmDnVwGHoTKh3OAlIbAZe0JTriuM9H9N+/F4A036IBqz7boNwmC2mLfANDGoqDrGVsYHp8W+5BmDzlkZwf3AdI907Inpo5HExMHyzXwvfnfx0zPwbq+e8Dr1OqIELv29iVJjhWlMh6WNkMP7H0AD+x9AKvfGFkGqXovfsw6aMO+UUzP58HGpJ1TnISpsiM7HPh79hy6kcTBmKojZxsY98JUYgaI25EllTAVP/18qZowTNqRBYYMiKfVhImRMEqZrNU4rcLO7w9b/oB/v+jfAUREaZyE6drDwBYn+/7137wNMwfVCBrCkV61BddKkq0QZQbUfFhZemWLm4HQQ2BwPWbkqlSznu8DcEA4A5XEx/L2+aDhQQDA+GiStInsyJL7VySJGVY/+6XKWI3XuW481mrj4rCCwMojMFw8svt+nNF9BoL+DSBE1DUy7PokTEu5gKntdkxlg1p0LwBwL3i2C660ho1bR1pV8wg7ts7Ml/owYA1rEkYlj2jIc29LWzUWtsWUMOkkjCXru3hWDh4huvYNNdPn+8QwtOJRgTZY1xwrnGxP4wZVEnqSrVV2iw+k9Vu5nP55FRY8/BBYpQKzI3IAmLTigsP6bhMnBk0lzASDEIKsbZ7wf8fTY767uxt79+5NEDGPP/64ft3b24spU6Zg8+bNmDt3buLfrFmzjlu/mmiiiSaaOHzQKmuAph1ZE42Q6ZXFM7mls+sbOW7YMuvYCl0YXC6cGhQGz5rpsvs4LIPCkotvFguwqWw3104JSrf0Aguuwauq7GCqayJV47zZQrWjyJhDK2GSNWFoAxJm1paBRKFrZta3I0sDt098TRiDEhRl9PTgfrFYNmT9BbPKz/zyRb06sxWgwE3/DMaJPje5KpWFecF7En+XM13Rwr/OecxmxfkOpP2YVsKkFPtRdgyWL/qd8aPsQaPONWkbNnwZR3G95HZOBqi8zqbuieocmXZK5ikhUaCG2aAqQ5aHIJTqwEblmY3gVYV2R2+VGdnm8V1HPFdx1owOLJpUOGyrohd/YBl6ZxaQUTVXUhIlfGJhwaS26I2O2TXb3zz7OhQzIvhDe7pRDTppEbIt43AqQwCAktsJRqpqwlQha3BMC5LXj2tESXvKEvvhbUJdNdIyHd7DD4Fzrp8fZSsW/DrtBQCATPkgpu66AwbzE5Z4xKw99qn/P3vnHWdFebb/a/rp2xvLLkvvCoiKRgWsSKJRjLFgQVGDpploNNGoGFs06muPRn+CMRpjEvV9k2jUREGNiiUWwALSpS5l+6kz8/vjmXrOzDm7sMvuwv39fNCdOc+ZeWbO1Pt6rvsuCVpF7XVwiLXZ1wENuYE0yUxByfH4SgwBgSIUbzBqvDgKnZtFqk0njHltBwDNKjLuXr7E2dvSsDVr3VwIkuQTTDNQ0h1oW/8spi3+IXQvIZnoVUKyiO3JQVhe82ZeAcbrzN7w3oPW3ye2tRc8+9teex0AIG61hZIP1u0E5xwVnycdmZPA2LG5M+FIO2mg5QnmdhiCSHFIMtzIXXTC6P7t7ZowWU6YPOnIeI7d51RNtfqjqx4OWA8njNdAioXRIvA7mBMQui3CFAUl8LoG17nu/NNKgQqgYyee/pYtwqgZzRUnM2vCWPdhAFzMdsLE21rdnbKesd3HGm+lSHQ/Q5nPS6bAgzBz8JgDMlRRwQMfPwBd1xGPDrKXl+WEMQe/AMD0pQFc/63h1rLTgzye0c3nM01HRI4YfWSzlFG57j+nmK40HYUDkilIKrveZTvETUwnDHQZqiH08Jr38WS2zYhhdLSI3nUInQhizno5j3vHXiNLhOEDPvcNczBNOo2tt/268GLDYUuAafjTMyj//vdRet55e9ZXoluhCAxRkGnTpqGxsRF33HEHVq1ahQcffBAvvfSSq838+fNx22234d5778WKFSuwdOlSLFiwAHfffXcv9ZogCIJwImQFNcgJQ+QjZIgqgiZaIwB13v9lZddmFvAetmOilY4s+5hzYubxNkls/aZnuxrOzHNtP7KaL8OeNTIMzjrwTNc0XyDOpRhB7zYjAOGXrs8MhKu8BN3Rl3xOGF7X7BdLAGkl7tvWCz1aWLDqCVLGPmnawfprijBClghTFJSsN3Gd46GpHLRU0hbLJHcAQahoyFlXIRGmrILtg6RSaq0HyC/CyGk2ojKUtlPL+F33JEGyRhYrfcAJIxh1mUpb7aCMp1DCcdbITkGTrNRUzAkjQG21gz3qjh2uryZWrmRtybXeYyh5Bjs8sc0u5n72/ENRO5LV0OJ0032R+10ePIZVZhXtNsK8leJKa15HmI1IDpZ6B3WkSMaqm5QIlAJ8VjqyLNJFg/Ef1R3gVTzEg2ZjhLsmKEiLYejt7dB5dv63BRxpgXQVvzjfPu62X3qKazle6chiAQlbdHedMfO6oXqMZo45nGNn1dQjreuI7mDtBC03sG7WHdAdYpCWyS32DQAiZwfyErKOoA7EJOZa0DMrIXnUqnJ9X1MhqQkW9O0FkZ3Iz7ypQ5BpnmhNi8b58c9lW/DMe+vtwvMel+Q/tNqp9c5oaUO40lsYMdnx6KM58xSBR9mFF1rTZoBfV1VrNLzTCfOH6YYzotJ75L3a3OKebmnxbAfYA1FCkmA5T/ycMBnLCWPPE8H5ts9OR5Y2aiulvNKRGdss8Bwe+OgBHPnMkdiVYim2dM3j/DJTeukapLo64+/c65mcAQKq+cOpkIzUVbGghO3BInDeGgzMsGl8wCHAsGMRj9kiTCiVcAlP6VSWIxWAVl5h36tT7nR3VkrJrOuYmTpLynHC5E8vZooXb296G6mMQ3BW3Mup+/o1FDV9BQDICAGk0wkrNSwfzH0uMJ9B1F27MLp0NI4bdByOGTCNfebzHBFtYalhg83MfSRqRio3lT1oRaax75vHuFMkSUtM6Od8nDD29spINkv2cyHv3RdO4MGrbidMT44/4ZT81/bsZzo+6OeEsY/jnU880aU+BA88EBU//IFVB4joG5AIQxRk9OjReOihh/Dggw/iwAMPxHvvvYcrr7zS1eaiiy7CY489hoULF2L8+PGYOnUqFi5cSE4YgiCIPkK2K8FvRDhBAIBkjDgUNdF+kc1j20+0sxeqoTsmgteMl7gCx9i0umkAAF0NILPrSM82Wzk2ws+Z8qAtaBYq91++WQ/AhC9ganCmvgDyOGHMoCrHQ+eETjlhALsIKQCkA51LKWD1JdA7IkwmwLattYm96EopY9RrlggjCzwEs1A1x0NXOegb3rMCpCHZnWIse7S9lGqFxucXs8xUXHY6MiPo5PXib4p0KRYUDaWKrY/8RBiRE9Gwjf09+Su2nYGWrv1O3YloiDDbhAJpcHjeCmaImmwJoCz4w4Fz5EbXs45xuZalliIRpueIBd2BD+cv0KbZLpXiKvsct0QYjwjvRiVX2NGN1/mhgXdQJ/3Xmq9BRajEu9YPxwOKIcJ0OFKm+KXrO2xYBWanr0UUw6x5zlQzR49iy/jU4dZqKh6K9N9uQMZwPVYJdvDr4JqDsa7W3jeTDpsFznFd0T1cX7LIo8NMP2amZzELMXuc1kWREDjD3SJpCtas/Duay9g6OpSstHA/vsUSWsxlAoBqxf7c56HgcMKsHMBhUjyOqdHX0LD2RWgdT0L2qFXlRNJUyMZ5SyJM3+OgQaWYUGen8pF4CWu3t2PeHz7Ez59bilc/22p8kvtM1KbZKdtbB07C1cd1rqC2k2RGQ8nss+0ZRoC6/d13rVkZATj+ywtxytLLMXIDO57bXn/dc3nZThhnKspsTBEmKDtFGO/7kOolwuj+7W0Rhq0jbThhzOdHr2ULPIdHPn0ErelW3PXBXZg+sgK6agerdSsNmfl/FUIpE2sF48ExsuN5AACvpiBlAM1wa3PIWM96RUEJHVIQbvuLw4FkCB9iKAZIAYi8Dk5n/Q5l0miJO9IYppNGX2wRJsMBm0rs7Yy3OsWAXBHmywlRS4SRM+5rhHmNEh0O7wGQIKpsfy4ddwmgA80d25FK2+vhndd3jgOva6je9j4A5p5JZRL2sj2elVLrmIOo+YUXoLW14e5pd2PGl+y3SHzxRU57DhwGbH7bmr6ztBSCbrguDUGqbdEi1/HJQbeEkrRxHY0r3u8etgijIPHfiO0QikQ820MQIO4FJ0zlz6+GPHgwKn70o/wNs9ORBb3T8eYb5FV28cVd7h/R+1AEZj9mzpw5aGpqsqYbGhqg6zomTJiQ03bevHlYv3492tra8MQTT+Caa67B2rVrXW3OPvtsfPTRR0gmk9i5cycWL16MU089tWc3giAIgugU2alsyAlD5EMyjhdeF+10Mnqrb/thB9mBvAqFFV/2G1Vtcv/R9+PZmX/DmVX/DwsvOMSzTUJmIwbjQWOEf1UKmhFkV2T/4LGU9Vl1cZVPS0Y4O1c2791353mk8ZI1Cj3fSxIAyCl732UCXXPCJMtjXWrfXeiGCLPzsyYAgJRigRU56g4IDK+KWMEDHUyE0Zo3WUJJUMkSYbKOi7QUtlIA8T6CgLnfN5WzdVuBgnx1hxLM+RFL2r+933XPy2VS9Fl2PY+9R5Gxj2NGrvg1IZ9AgcMJI2qSJZhyusrEFedxmZXSw0xrg15Mu7av87PjR0LzEFPeaLnI+ls+sAR/et8uBs8J5m+Ye2w3CrnHqh4x8vBzOoYq71jzY/wm32ukrgGBRBMAoNVIVyPyKUiKd3vFCN5t32aLMCHJDnTNnlKPuUcMxlHD6oAMUzO3Vh4M9a3fI2PU/+IkOwAZk2N466y3UHrp9xA97jiEJh+Ehgl2UWwvJwxgx6zM665mXAe8Qr4V0SIriFnZJCG97C/ICGY6SffyO0Yd4BBhHNd4yzHjXkNQcAQNOQlJjkOQa8eQtf+AjtaCThhBVyEbo+h1iUSYvogi8lAT7Nw6oeEErNhq38M3NbF7uF3Jwz6eMo76QclBh2FgdGCX1718U4v7PDedMI6avHImiiE7D0R122BUt9ZlL8KF1uIWYbY//Ihv23jacHfIolVzeFur94CEjKqD0wHBsf0SgO1t3qmm7HRkbB+1BnYCAD7514actpYI49gPHZkOFAUljOoI4dSlP8HAppHY0r4FgK1fcLoGsZSll61NAkNSaQRb3mTrF2QEkwFkDBEGumqJDbGAKQo7hBdnjRjjuhALFwO8BEHULCdMLJNES8L+bTIZI+2heR8G8PfVf4cG+9nPJTyZo4wc6cgWnzfRSkcmq7aYruu6/fzjEHCfCY/HjqgtBBUlKqGuehUZxzEjOJwZnPFdIWO7Z9JqynLCeLnBrRSmAJKGk7blH/9gM1QP9xPHIdzO3EvNgUaImi28Cw5HyqZrrnV9zRRXOkLsHvPJoFyRDgDaK6vZqnkZejtvO2EUPzGDz0lHVuDRfbcomzMHQ196EVJV/veOrqYjc6KMHg0ACB3i/e5E9G1IhCEIgiCI/YCiCvfD3bplO3xaEgQgSaYTRrJG15ujF7049OQhAABB5lAdYC9Gfim9nIyuaMAvv3kApo7IrV0AAFUxNhIuEeDQcFwj9GOarboX+dKRcTznEkzKIiW+bQFgdI1b6CjohAHQFh5gvZgXcsKIjiKkmQJOmNGlo7FkpL285tLeCdJFjNcEtTUNXdMxcKchwsTco/uHlEdgBil1jkO6XUDGWRNGdo/GD8WytofjkZLZ/hcF75H7ohFMFjRThDGcMHlEGDnFAk8B1a4JU0gs6ysEQyxIVGYEZ4oz3v3uePddKxA9sHmEJZhaI3AdAazsmjDbH/ota0tOmB5j/MAinDiuOmf+0g47/eLCVVvw8+eW4utd7BphjszlPOwdLR4GC71hmvE9DcODb1rzNT0N2ceN2LYxaDlhWqKs7lAo6n+dKQuzz1JNB6OIH4LE1m8i7KjrVRkN4LpvjcFjZ5yNlRWfAwB2loxC+w7JquPESe6UMmEpjKofX46B998HjucxZUaD9dnWEm9hULdqhBk1uXjz3pS7r4qDQUugPOVdEc08bxXA1rJcnRzHAqaAW4Qx4+nZo6UHnXua9begRZHiOMBy6QGSHEY+1sZqLCcMR06YPkkirSK+/kIM4+fgF4f8Al9usUWYlrg7KOw8+tKafZyPr5/mueyPt32cd92tibQ7T5IhBorFxdasQzbaKVf/cWj+55sdj/0/13Tz88/7tm1P2k4YM63Y2u0dnm1VTUf2mRrTOKxubPNsn52OrLy91rcfVk0Yeac1b8nmJaguCmL6xqNR1daAb31+GbZ0MBHGmY7MdFZ0fNKI/924GYKWtGo+hVJBqEYVGw4qRCPAXRph52GbZAfw3bWmjGeOQBAQJGyICZYIU53h0Ow4JtS0KcLYTpgZDTPAaU1WG3cdHOO+7bguHTfsZIhGGttYwvF8rKqWCCM70ouVyDHwMfsYlVUFyfbtSJu1vHQNvEOEKf7udwEAgmqmt1OQySTtuioJe7970fTXv+b93MQaJKLKLhGGdzjG2hYtcrmOzD6lBXaP0XjvdGRShP1WqqCAA2c9C8l5Bpbk1ITJ4/DvabJFGi7g/fzrmTPNeJ7jKKtFv4R+NYIgCILYD5Cz0jO17eq9VDtE30c03AqCJlrBwHwFagMR9nKlpnRkjNohfJ4AeWeRHEJLsCyNtMRZI8QDHilrnGRSdn9V1b/vABDIcmcIPsF65+jUdYNOcNQyybt4S2QAgITSnqcl8OAxD0K75Uqc/1MBZ/xcgKr7i189SbrWDkb89TcfWoGmYJH7RbGuNIi0WTeI46CmOWTA2YEC2S3aCCKPuXcdibl3HQlVZ8GdlbUs9YsoeAckTRGmOewWYbyEPlNoEY0itaIxmpnTMnkL0H+VGyvvNZSA+3pdkvJpCGBLzWEAgAEtw10iDJc9etKjDgYAQCQRpicJBRwBeZ1zxpoAAEkjCDTjnjfxwGsr8foXLNURBx6tjgG9/5zE4bD2spzlW2486JD4JCoaPwIARIQlloPFCyXZBABIKcWsn0Xeo4cB4ChTJFfDGJT8BdI7j7RSFTnhOR4fDHoLAJCRQti6uhoZgYkwgpT/OlZaEcKdRXH8pjiOtF/6euM6bQo75nVA93AbRUOSNdq6uF1Gi8CD07zTl31jaDk2lbBltkQdadHMLmfVhOHL6+2/ISHFcdA5dm3SOEAWvfdl6/jxAIA1sRpIphNG8Qm8Eb3KhLpi6GoU9dLRCEkhLNtku0neXuUexOQ8pdOBYuvvAQ1TPZf9zuZ3POe7luesg2deNMzUVoqIgc2jrc8TAeYYTodkpFVv10BnaU2w47IoKKHMECa8UlMBrG6LlHU948GBU70D22ZKTFOEMeuDAFmuEzhEmPBnrvkXHzkYnG5/78pFVwAA5LSdjiy1bp1jucC4ZMpKcTV1DQcN5rVLtQbcRIzUXqtL7MC4bjw3arpmD/wIygAvYmlAQXPRUABAPFTlEub0ljZzg6378CnDToEqcAi1b2bf6bDFAEtwd9yyjx8yA4EEc5GE03ZqPD2dtsRn2Rm0374SnGQ7ikRNRoSXLVcOr6uuuiDFs1i2GjHD3DmqGEA60QqNY/tBTDQim7JL59kTGW9hxAXHWaKHpCl2PRgt7Ra2Nc0lwpgpwzSB3TszfiKMMTBMtQRwtl+kfiLCVF55hWua97kXmHWgXPNMZ7OPa5/o29CvRhAEQRD7AdkijJgnlRNBmI4PQRfBobATRgmKlnDT3Bh3LWNPkKwAPltWGpzlzAn4pM7xonJQ/pRemU7WhHESa1kH3Qjq53PCPP7jkUjKdiHWT9v/69sWACpCFbhw3IWIKxx0jkNG68TLbg8QKLcDiVvX2IV8w2XuF0WO47CDM7ePh5rioTqcMF7uk0BYQiAsoUNhIx1lzXDCiN7RVzM9HrjsdGS5y5YGsfRK5su2rLF1ZBfXzkbtQ5fEtUs751QUa2owYJPtfohsZkWgORhBDWdOe690IYCrbgzR/ahZh+jHHSe7phNGEKgtmcGdr6zAV41sNHNDSsVD37KvoU8ezaNOqMCOTW3YsbHNrvFj1kKADo4DJq55GEf/5wcQYxtz6sc4MdP1mQSj/seBJPA4YhhLF7a1hZ1X2cK1yRNn/s76e5c4xBJMxE5oDQMMIWRinffI/oQRuDMLT5vXAS8nTLCi3Aq8toRktPA8oJtisbttSUjGzpixTEd4xL5kZAXqJNtZzOtMhMls32n0hdUQ8SJlFE6XtbSVjoycMH2TYVXMQZk06pvEHXVO0ua5Zx1H9gGVVnMDpt9udTtDRpWMyrvu5njac/S7eQ3fHsygOWAHyUWNpW2SOlJ46JOHXN9Jb9vmuY5s0cOk1UirFQ2IGGnsg0Ta+96hahpE42RyphlNdHg/s2TXhHm/7kV7WVl1ZFSjf1rp/7rml0UUcKI9kGVbnO0H3vg6p2sonXO+vZwUB0XTLUF20tIAlsrs/FWhuwbc1BYHrdoyrL+snxkt4xBhQoAgQdB1dIRswcbphNHb2DHA6yo0o/6OxEvoUGyBIR53jqwwnTD2HFEUIGfYvUDSAtb1Xk+nLcdHCI5jTZDA8Xa6M1GVkOKAjCEkcZoKzjEgRjfdVZYTJoiN7z9qCTxiKPeZufTcc+0ex4pyPvfCEmFUGZJqiDBq1qiSrGcVu9YLE2FU3vv4E8Dma0LW4Jw89z0zxZs13amt6Bmy9yHnIx4JpaW5M81rEIkw/RL61QiCIAhiP0DMCpgE86QeIQjzhVrbcbg1ul7k/Y8ZjudQOsCdgkXwCJB3lQGlRt0B40U/BUAwRBhF7nzwODsdXzYT64pd0/lEldqRLEAoZdrzCg0mWwZF8fmATdZ0h9zi29aLPR3ZursURb2jpuEyj1HeZq0GjseuFWFkdPuFOF/9KclwfCgZJpRIgnfw0kpHpmc7YXJfZTiOQ2zmiVZwQYZZrD7/SHy1D70VJdo695sHRo1C5TbmfIhVB8G1DwZgCE7ZQTwfJwylI+tZJtUXu6bfbr3AnhgZRVvWcbdDZ4HPIg24kLdTwqQlDlIyimd+9R6euek9vPA/H2Hr2has/MAdZB32rW0YecpG8IKOWNDPUgJE2je6pv3qwZjEjIEcX21jAWUvJwwADC0eCrntPQDAymGnWSKMnN6cd/kA8NRFh+KeMybgsulDvfscNhyX1shn24GXDcfzaDcEjuaIghZRBm+k99OymisSD3vwvlOEMRtmnTsOEaZhm4S6DRya/70EAHDICh2yj6NPk9g+lNWMlY4MJML0SQJGIPefy7dA13UkUvb945MNTQDg4b8CEi0bc+ZJRoB5YKOOH/2vCnmjLaCY7oRXD/22NW/Tjhb39dsMUBuigMoDkaQtVCqqHcx946VH3Sv3EVvUHd5Cf7uxnWFZtJx0yYz3vUPVYKUjE2UeuvHMl4z7iDBWTRi2vKZAI8xzK5Vw358zPm4aAODE3Gco3jhXdUFH2QX2NVZNmKlMWfB9dY0MXmO91qFBcjxDKCLvFmEMoVRNJi27sxwMAIKMa3fsxJA1f7PaSh/Y4rPp4uE0FdJAVhMoo2WwI2qn2krEHY4Ms/4X795mWbPbpI3fRU8mLPE5IDqeE466CjpvT4uaggR0qGbaKl0F56w/Zcw3nTAppQhvtfD2AJf63FojYmkpSs8/j/Uj0Ynahg4nDAce3/3k5wDs38IPy61i9EURvQUf8zVWzRJh/Jxb0HXwuuZKhVbIxd6jaJ1zuQvRaO5MKx0ZPb/1R/rQ6wZBEARBED1Fdhqe6efkH4lH7N+YI8n4TMRynhQHvOu2mJgpyUz4bshVLGeNxks73pgCBdxc02aPBACMOLRAcUzY+cBN8jlhoqUsAJgRZGvUdD4RRhEVbCj+wprOCPlfQLMJSvkFpJ5C1XSsE90vib+LJqwc6q62jqLWgqwbThj2++QTtAJGYFfU2bHj54QxRZiwKlrrAbxFGIAFtgQjHZlkKUT5nTDZxbp7k0O/PcQ1vT3skw5m506IqpFOJKlZNUA4Xc0RYfSMzws/pSPrUSoitmjpTJsVKVGQHp8bXPqcY+nztqWH41DouO10Hteda5xLO8db7TatbMJffv2BNR0vMQQ4HuAFIKHLVi0XL3hdw+QPb7f7U5q/jkkg6zgJ5kl1tqJqDetTqMoapayo+WsMAEBDeRinTKxFyEdgD4bY9SFj2GrMEeEe5XMAACnjeiKrEpp5O5Vl9rleFJSsZTidMDk2JhPHNVnUZHz/WfujihZA5H1q2hiuM0nLWOnI/FLQEL1Lfald82hLS8JVeB0wHAYeTpgOj/ujXMXO2/lPqTjiMx1FV95tfcYZv//agVU4mPsC5wkv40vlfDQtfdlegCGgm+mx0lIIsmZfV8Ip+9nshqezrvOOtFE1t95q/Z1YvjynnwAQT7H2IVmwhKikrxPGTkcmyYKVLjCV9HPCGOm9OMu2Ap5n9+lUwv0dzUc8AgAEcu/lphMGnA5OFCHVs5SB6vDvAODAG4H9uh2K9UyrQ3M5YTY1x10iDMz9HXc4TIIhgJcQ1XSEOljqSEFN4qRN9wLxJtZ3Y59zUK3zuzpcjYRsp0lNNNpinek2z36OFJKq9dxiptfV4+123Zah0xz7pAgKZx+jshpAArrthNGznDAZU4Sx3TQXvRqxrqliwKMAGZj7FgAy2xqR3rrVml985hme7QWH64Uzn5e93N0eThiTmOJdO0jiDJGMd4vyfk4Ysbo6Z/l8Lz7/ZD+T+bnTPL9rDqrpVRWJ2F3oVyMIgiCI/ZBCzgBi/8Z0sQzMiBA07xfEbLJT3nWHE4YzRrRxxktzirPXUUiEGXtkLb7/8NE47oKxBddTlDVi3G+UNwBIRu7wojkXQxnF8rLnqzVSHarG51XvYPGQZ/DHCTfj6oOvLtgfAJh/2HyMKBmByydd3qn23c2E+mK0Z+XLbuV1z1GGsmSPSG+N6kjrnJXWIt9xIwbc+1mRvIPGopGOTM9Ecf5PBayuNnN/+4gwsmw5Yax5BZww741w97PtMu+gwt6gtMYdEP+s3sfRIIpWECWdyOD8KSwVG6druSOgVe/AGKUj61mcbhRngH/oQZX4v4+ZQ27sgBiGVLDffItgBxi/iB+Dj4bx+HKgUbdAHey7nqp698jkRbUX572OAUCsdT3GL/sdqra+j4O+OSxv2wuPcK87n8tmZcXSnHkfG6m49gTBuA5k1wDwE2GM2wYkVcHCWBhm6EPIqm8WkATwZvDcGdQynTDZdQNE+/lJ9XDv+TlhxHbmIpr+9UcY3LLFWJR/LR6i9zhksJ0C6OFFq/DFllbX5y8t2+L5vQ6P+13xiJkAgJhxigo77PoypsDy68RNeFr5FW6QngDP6Yi+eKktpFtOGHYNT8nu9ERRhwiTXXpJN4rE85GIVQcEADZe+TPv/huOi6AsWE6YRNrPCaNDNk4+URbAGc98qaT3vVY305HB/jwtsJ2SznbCGG4SIV2XuxzHs2Z1Skcyo4I3LwLGuS3E2AAeNVQP6IBguB9UXnaJMJLjeebghlLwjtSDZt2NZIed/kyJhACetVoxwBABBAW/T50F3M2eBzVDtNIdDrqQFMIJZUdYTpj4Z/9ybJE5mMe9nRx0q9+J7U1sP3W0Ws9WoYhjkFL1eExK2seonFGQgAbV2Oe8rrpSH+pGrRgxY29bIlAG3RCQJR+Hnvm80LZ4MTqWLLGXF/eoM8px7tov5nqCuQO69DwijJ9QIgdZH20njCFO+ThhhGgUYmWlKx1ab4owYplHmrFOoGuawwlD4fz+CP1qBEEQBEEQhIu2XewlqFYVrBdWTsg/SksOZIswe/6YaabW5400VGndfmHyc0HsDuUR92hkv3oHACAHWR8ynAwuzAKn+VJuASz9xudV7+Dq436CM0Z1Lrh/2ojT8NeT/4rqcO9UjA9KApqz0mNoHLydMJwZAObxgRREHPbvlG/fyFkpkGQfYU0y5vPgEZc5K02F3zHmdMJY87JTCmXxyqSsUajFnct53hPEW+0gQZzTofukikqtWmUFdVIJFREj3ZFX/RvfmjB5itgSe07METB8uv0p6+9QTLaCnAc3lOL0g1iwUeWAD2UW3Hsqbufg53QO8Y5iAMCZ1x+CM687BKOmVGPY5ErMe2AaAkXFVtuGxNMYUOd2Uzmpu/0a6++K7Z9g4sZHc0T0bAaXu4XBfCJMW8CdLkjXE/gwsc6ndefJGKd7djoyr/oZgO14MQtCm2J+h5A7yjtoCMAaeGDHKtbeSF+Wc+0QRJRtZ0LTwK8XuT7aGfGvCZOsyr2Wy33HgEc44DjOEkafeCf32H39CzsNYIeuYfIfJiOtpdEA+7f/5N8b8Oyt7+Ps4bMxqWKC53rMEe0ZDji2vhan1bJjRNdhHddmgLr9nXcAACUdLHgbFViKv3C6GJ+NPMd7+aYrIyttkdba6tUccVOEkQSrplTSx0Wp6joCxiNCICyCN56bMj4ijJmOTHPU+GgW2b0ulZXCzGwbUW2RMmiKn4Lt+AirCpZtbEFAs90ngEOE2cXSrplptzJiEOPXGU5vze2EqYgq4F01Ydh60u22wC1LrD87BAEVzfZ9unXnd9GixoFEi1X7BlkDP8LBIvBGPz9wpuTS/R3VpiCxZeHvAQAdbY76fEHHNZnjUBO0+6lkgkjoOjTNURNGso/NwBizfhwQbWHHd1oK2+nIfAbEaG32cZNat97629MJs5vXNjHr2Y33eUapvoAd8xkpDI3j7XRked49lJEjrbo8bNm9Fw6XBw1CzW232TM6aYTREwlbtOLJydwfIRGGIAiCIPYT8qUFIggnww6yRy2bIkxY8chL7EDOcjV0RzoyM6uLqstYJstIcY7gfjcez5LAI+QQAJQ8o+MCRl2CREcGdkYA/74MLxlu/X3S0JN8A3R9jYAkYIOYG8z3csIksAsAG4nIZQAk7ZfmfL9TeYnbkSf5iF9mvQoeHERNttIKST5uK06SPJww+UWYaYOOcU3L//k4b/uepG60PUJSg3+Oc7WpyQouQQdSKTv1SDZ62tsJQ+nIepYSR6pDx4BqjD2yFikjuDljXDXmTR2Cly8/Cu9feyyuvPBAAMCoWATPbGQj7u9YLwEZHaIioKQ6jLLaCI6ZMwYnXDQOgshDyRq5nO3ucxKZcaprun7elILbEVZEnDZpYKeWvzPintYRR7nH6OeuMrCSBR0zArtumCPC/ZwwphZc0WKkOzRcnarHNVi05gm4+Wl2LTBFmJzl8yIOWPYIDn/nWpTt+sL10T2nCJB96qe1TpyQM0/Wfc5LotfJPsb/3/mTcd23xgAAnvtooytlVlJN4qTnT8Ji4ysHB0fjrT+vROP6Vix/eRuemPmk90oMcXy9ImKnIGCVLOPlUJClBbOcMOx/Ox59DACQCLD7Q7m4xlrMlprDkJRzBw60G24FtbkZz698vuA2m06YkCxANu47Ly/f6tlW03TIRqRdDoiWU81PhFFVM22phsOMFF8p0fi/jxNGcRSfT1nBeXu/q1oAr3621XLC8DoTN/giJsJoYSZGi5kOAMCukhGW0FDWGncNKglIAnYE7OdczXhmSMY7rHmKIWTIuo6vqt19buQFQE0BxnU9W7yVgiFLhNniCLgraf/6eWYatV1bmeDW3tJmfRbOctGVhxqt9komgDinmxnVwOkqeMc9QoiEMeKDDzDgjtshGW6YtBSBZlxTw8Gsi7gBF7Cf2eIf/dfuZ9g/nWWr4O0ac+HYH9lOGM7H0Vk8yE413B6qsdOR5SlWz0mSe/mppG/bvUHxqad0+TtaPE5OmH4O/WoEQRAEsZ9QaKQrQZiUVLMXqiR0pLd+EwAQlPOnTSmrdb+0dUc6MsEIyouqhLNqq5GCfQxzBdwnXaXMESxV8o2kM+oSJDvS0M0CrHm6cvaos/H9Cd/H0zOf7p6O7iWqYwGsc4gwLYYrRvJ4wTXr3KiCDDkNcGn7WMn3O5UUux1Iss/LtrNo+MhtU8DDHPHo3V7XdcshYvWjgAhzx9Q7XNN8L6bpcrrKwjoHMc+LNq+lwZkpSzpMEcZjWykdWa9QFPIOyCtBEa1GHQRF5MFxHEZWR1ERVVBbywKIqdYMxiRTWLpmPerbWKq5yvqop7AZi7rrZ8UCeX5XxX2tlo65rFPbcvzYKvAcGyV/QK2/U6woUocRK56xpnU9gV8eem2n1pEPuyaMIcKY6ch8To92swYFL+PkdzVoRpBO97iGxTPmPuXxpxgLxJrOmZzre8VocNARSDblLKc5DAh+o5M5DlwwS3geMdK7LdHrmK4Qk4byMA4fWmZNp8wItyFEbGyz63yMTduDL+Jt7noygO1uMZ0wT8Xsc/LKqgp8oIgOEYa1CR3GxNJdRQMAADGhEUWCvc52D9es1s4EhIQEXP/29X6batFm1HPZkvoUv1l5MsLDb0ZJ9XuebZ01Yb5s+gTpLeyeW/21d927tOHK0aDhsroT2TLMmjBx9742nTC8o86JCh3pVNxlGMhoQTTH045zle0rPsjcbpouQ6+ZgK1VrND81qpDoPGmM051DXDQdeBPo6ba00agO91hP0tIRl2V4akU/nGw+z7LaTKgZaBnzPnubeI+/BScIbqmDIE3vW2bJR54PSuZro3WBBNKEu22EyWUXU9KVDB47YsAgGA6ij80L7cGCvG62wkDMCGGkxVIaSbsOJ0wwYC3CBM7cYY94bjOeaXkNedlMu5lTVlyo8eS86Uj876XOZ+TVDHgcPHkF2F45/ILPBfuDcQKNkAhPOXQTrXXOjqgthiOqDyCE9F3oV+N2CssXLgQxcXFvd0NgiCI/RsywhCdRDJcLTI48Cp70ctn8QeAWLk7uNQd6cgixou0qMuADrQ6Ch7nEz52B6f7JZ8TRgmxPiTaM3agII/QIAkS5h04D+Mrxvu26YuUhmWAA54PJzHp24OxoIQFVrzybY+umAiApQmSVB1pxR6hmM8JEwq5gwJ+Thin00jlNMudJfoIfYlly8Hrmiv3NwqkI1MEd0BDOGdW3vY9CZ+1XX6H1+D/+19wAKS0EWgzAlme6cgyfiIMOWF6Ep7n8HmZ+wecfs4obGyKY1urMWo563oTKVYADlDTGhI6E1e2plmwvnqot/ghHnQuVqIev82cBAAoCuV33FWez4KgA6bsstL7FeKEsdX46Prj8eF1x6Kh3H/k87iSg/H2iM3WdFNwCySP2ildJWQI5TvLWCqdjnEHsQ98bgZVEcP5IigYs063RRuPY76m1BCxjICopmuA5rNfRG9hjX1WIDWlo8g3AMRq9rxWDtEznGPU2DKJBSSMronh6FHsN+tQvVN6AcAYcaL1d7wlV5TQ2jtYvRYj0P961J0ib42ShhWYNgQbZfAQ6AC2V34DABAVtuOs8h9b32kP5YowepJdYxaPY8flm2Pt4zP51Vc57VsSTPT4/VommvJiG1ojf8aG1g1QtVy3imQ4UDakbLdDOOGdVyljijC8igGjTwEAxEW7pln2sgEAnHvfPf2f+S7XhK4H0RJPIxA3ngnajGeykCHCdHQAvIiKxo+s77RGWepHTlchOp4tjh9bhRbZ/h1MESZlijC6BsEQMiZOvhQJ2b2dqh4E0h2wdlOWI1Xd2mg5YdI6e47Umput6xLvcR0zn2HSCXbdiLfbdko5S1ThJAVKktUbCqWNdGxGXStey4DLFm1YJyGnmAiTkmLQzL5I3tdrqdo+xtrfesuzTTZtui1ilW//FKH4Ngz4zW982+c4YWT/AQUtyk4ATJi30pHleR5Prljhej5KluTWHNrbDH3lZQx7YzGkmhrfNs56PlprK/SEeUx2MocZ0acgEWY/g+O4vP/mzJkDAHj99dcxffp0lJaWIhQKYfjw4Tj//POtm+eiRYvAcRxKSkqQSLhHGr733nvW8kzOOOMMrFixYq9tJ0EQBJHL8MksMJrtWCCIbEznAQcgYKh30TzpZ5zfMckOJO8O0ZB9rAqahFeMaQ2a58i7PaHNEQTIVxPGTEfmcsLsg6n+JIEDxwFfSRqGHlGDjDGkUvLY1jFVLEWLKigYswGAZr8Y5hNhst15ik9NGAAor2O/fXzXYeA0o3isj9CntrBAhNMNU8gJk41Uvufpk3YXKWs/tKe807sERowAYKdaibebIoxHOjKfvP6Ujqzn2VDtPs7HHDEAn25osqbN2hMmgsQjFGNBl1a1HACwK8NGv5cN8BE/lAjmhu7D7Zmz2Dpq8tc0Kpt9BkafuQlFDXGgamynt6UoKCGUJygGAFMGHIxXD2i0pt9reB/lwfJOr8MP3lEgfNTnn6FlOit47jfARDUcLaogY0OFXUNG83B/iQpz78kZsw5GApwpwnTl8l7gvic12IH9pKjk1Noh+g7FWUJmWZidkzVF7FhJZgn7wzbquPmJDM77lwpdt4PVu7Z2IJvEsmVo/fe/rWkt67BJcRw4I2BvBlr1TAYp2Xa8DQ4sgcCp+O+AVwEAHR4ijGYE71PGpjw91b5n7nzi9zntm+O284TXBBy4aTqiiTLMfG4mJjw5wb1sXbcq4KSFFFaVMaFjg+x9r00bxeAjiQxicj3rl2DXNHOiGtsc5937bnnHZte0nI5hxbYmSGnjmdX4njm4oOXvfwd0HWM+X2h9J6kUG21V13OkIvKsJpS5fcb+T8XtNGiCee0oGZzzm6W1MPDMOdZzYc7AD0EEbyg0kYTRX1m202h5PNOaggRn7IaUKeLqWm5dxB1fQk4zYTCYZte+zcZzLa+lIQ/xqBOm6wgkWN2ceLDcSvHYPQXr2fZsEu17gfkcFj3Wnf7VKSZkizCC5H+/SRlOqbQYsvdjHudwas0au5YYAFHq/Wd3PhiEVJlfjK+86irr70yjvT8z23f0WL+InoNEmP2MzZs3W//uuecexGIx17x7770Xy5cvx4knnoiDDz4Yb7zxBpYuXYr7778fkiRZxb1MotEonn/enV/08ccfR319vWteMBhEZYGLy56STudafQmCIAibw2YNxTHnj8bJP57Q210h+jiSIliBp+9NaWDz8ggTgO2eAYBgTIbok1qqK8QcIoyoSVhvpKHRuO5PIXDuYXZwTM6XjizMXgiT7RnrZdtrBGN/h+M4yMbL7IadcUtX8UqNVV3CUrRkDDeJZARydOh5BarsOkJynmOsqIL99lKyEkizALNfOrLS885jnzsKvOYU1y6AJAcLN+ohuiLqcZIEyRBhEh1ZThjniOGM93MypSPreeZNG4bHowloHDB+ai0Ae8T59JEVCHhcK6OlLNDbpjIxsFllo2SLKnOLyps4xeMRVQUGWwycDBz2A+D0hTnpyfaU00fPxA6tFitL38Xa4k+wofhzjCkbs8fLrR1ZYv2tZXSo5kXJ57KRKWPXJZWX8e0lthPG65g3g8kcTBEmbtWe8jofQwcf7LnOgmkyVfs6FAzKeV2XRO8yqtoWPKaOqLAGFMSMASmqdYFl82/9vYoRm4Bvva8j845dK6ilMY6W7XF8PM7+7dfPmYOmZ5+1prMD+o7FukSYHaX2eRQTWDC2Kchqtng6YRIsoJ00DvCUQ1dKbM+t9cJEGHYfOfqrc3DYulPw7eU/tD5fscseVKtqOkRjF2T4FDbFmLMmCW/BP5Nmro7vvqFizbEn4+wlmkOEcTthVKO4vZrlhAlwkmvwv6wGsLJxF1LGtS89gg02a3n1FTa9cSOg6xC0DEp2fs76Z4gwWyPua6kiCtAciqv5fKcaIgynqxDN1Fhr3kB2ybzVQgzYttxhgMl95uB0dt0f0GQsRxAsUcCrjqIpSATamACY7DDUGI+BFhwHq0acpLL7h5Jm69H1DIRI7nWeUxRLhEkoxdA41r47nOymQzGk8HhfSUPndDSse4l9lJ1izPGjmoNKTATZfwCYHGDfy4ghOx1Zgb7rztqS3ZzWuKcoPWc2YOwz06EFAMGJE/2+QvRhSITZz6iurrb+FRUVgeO4nHmvvvoqampqcMcdd2DcuHEYOnQoZsyYgcceewxyVtHH888/H48//rg1HY/H8cwzz+D88893tXOmI9N1HcceeyxmzJhh5UNtampCfX09rr3Wzhe8YMECjB49GoFAAKNGjcJDDz1kfbZ27VpwHIdnn30W06ZNQyAQwB/+8Ifu3l0EQRD7FJIsYNRhNdYIW4Lwg+M4y9mS7DBG0hV4sXE6YUqr/QOFXaE8XI6UkTNcyYSsoJjOdb8FP+xwH0QU/8C00wmjqfuuEwYAkkZu813tdiDEK9VDSYyJL6ohwgSbWCBALyB8yIHOO2HMtrLOgTcCJYqPaBObyUbIi04nDLp2zMi9KMIAQInjHJp7xGD/hqIIKc1SlMTj/jVhdJ/BSuZIaaLniAUl7BB0PFWj4sgzmXvJHHHuV+A+UsKCaK1qBTSdR1wrMuZ7pZRhXHTkENQUBTDn8IbCTkFeAE64BRh7alc3pyCyKIDbdjH+PfKP+Ofox6GpJYW/1AnKKuxzsq0jDc2qOu29raYbUxPYM48V7PTIoy81DGVtORHQdbQmmqy0h/C4vgfGeruHdHjXwzBJb9hg/U2pAPs2g8vDmD6yAgOKAjjtoIHW/NpidhyqxnOIWRTeSXLDTtf0k798B7pQ7JrX/vY7vus2nnTYf00RpqMFO0tzj7tL4kxcaIsMgJ5l27KcMMa50OG4fLy14U288NUL1rSu62jqSIMzhJFBu9i6IqkSKGl2P2pKNFntVUc6sgyfQlpggoGgq1aqViemCGPen055TUO6gBMmxbP5l+1i63191zI4rWmiJkPk41ZNGDHMNpBz7gdjWbJR+yQps2vp10Vut+DYAXY6LgDQjTpqGSvri2bXipt8oWtfAsCDRUwoN2+/XJYYpauqlY6sfquxHlW1xeGSXJFE0Ox6e1jzBtJGXzwHlXC2+1fS2P1DNFzDvM+zWOSooyzhxllXpaviBBf0f+YPSwIWBTNYdwCHaNvXbKZPnRcAObW2BNn/nVUxntNYPRvDCZPHjcjHYmguHmb3ux/VVJHrWOo0PWXfY4QIOSn7I/3nqOsv6DqQat/7/7oxH2B1dTU2b96MN954o2Dbc889F2+++SbWr18PAPjrX/+KhoYGTJo0yfc7HMfhiSeewHvvvYf77rsPADBv3jxUVVVh/vz5AIBHH30U1157LW655RZ8/vnnuPXWW3HdddfhiSeecC3r6quvxo9+9CN8/vnnOOGEE3ZziwmCIAiCyMYMeifaDRGmwEuZM6Ce6PCuP9FVyoPlkI2XyRHbJ1sF2bUeEGFCDuElGvAfeWeKTboOpJLsJbu/jKbrKpPqiwEA7Sn79xQ9ApLmb2+KMGYgQy8gfMjBLCdMHhHGFL+Cuv0C4yfamPmznenINM0nHZcPvNS7YvWwg5iDvKgiiBPG5o5wNuFE0drOVNIQBT1GyWpt7TnzACDxyad72lWiADHjerKlI2U5ylri7JzyE2FMEW5rejjiWhF0COA4IBj1Py6/O7kO7/ziGMw/ufPpxXqKiCKiffUPkdw2A+HGa7plmbGgZF1TVm1thZoxnTDe19+BlSxApVoijFm8O/e6UTlmsNFWAa8DrfGd4I10ZJ6BOp+UN3ky4eSgNjV1vjGx1xF4DgsuOARv/+IYnHzgAGv+YUOZw6rJuO8FPHS3tvDAnHm7Sm7DewddDZXPDUC3B93H8O+KiqCrxsFk1vPa+rl1LL9X93er7bEqc6dkpAiWNriDsrrh4jCdMKrA4alpbLmTv9Lxq0W/tNpuamZtxdinEFUZomZfm2pbhgNgqWBNVM2djizNsx0xOB2w6l05yaSM1FqOe3GaN0SirGdG5nLLIGUIrAOMdJqqrsJp8RRVGSXcTpheNjnM0nDV3HyTvSzjPJOMVF1pmbXROPfJGpAEVBfb+0837qMZR00Yqxh9uAK7ou7fTDbcJ7pla8oSPhwijMabrgbNui4JHsKE6YRRBQV44iTI731hzPdzwjAXiaQGAB0QVUOE0byfyTlRtAarZIQANN7sS9fCxPLAWo+FG04Y4zltyWrm3FJ5IXeQgCOWqSR3uT4SFf97XsRIz6cKikNk938ejxzxDdf0pEO6Z8DYXsG4uVgDavIIWUTfhn657ibdAdw6oHC77uaaTYDcPUro6aefjpdffhlTp05FdXU1pkyZgmOOOQbnnXceYrGYq21lZSVOPPFELFy4ENdffz0ef/xxXHjhhQXXUVtbi0ceeQTnnnsutm7dir/97W/46KOPIBlFwG666SbcddddmDWLFUUdPHgwPvvsMzzyyCMul83ll19utSEIgiAIovuQAwLaASTa2QN/IaHB6YRpaez+0fWTvz4Rq8o+YRM9oHmcMLYa/1y2BeNqi/KmI3Pmm86k9m0RxkyVs6PN4YTxGkWu2AWwAXvEuV4gbZyU5YQJ5BNhIuwZMaRxlgjjl3bCHGHuTEfGq11LW1scLutS++7moBMbUFwVcqVg8oITBCvPvBmX4XQdgdGj0b7kPaud1uZfRJroWYZW2u9oHakMogHJcsLEfESY+jGl+ODFtViZOApb08w9E4zKeQNMfYmWRBpauhapZC2Usvz1xDqLIPBIA5ABNDYl7NH2PrskYIrDfJYTxsOBEo0Ybj4xAEEDVu76EgL8RRiO975WyaH8143Km27Ctuuuy9uG6NuYomoTBqAOgJIBRn5tB5FVXkRHmAnnU88agcV/tFN4tUXr0Vw0DKW7vkA+Uo7z/F8PXYNxv/w1eEdNmO3hTYA0ChjzbQiLb4eAdqgIQ+SjruVohnPCmYZsW7H998wPdOhzdXAch20trG2g+n8x5fNB4Bzjtcvaa7G67BPLlQMwt4rkSEemcbYwsLk5jmqjdo7V3nCWOJ2aRW2GCJPthNF0y5EDAONldl5puuYa2iFqMubI/wCnfxc6ADnGNs5Z/yS9cSObZxSgt/YNlxsKjQZte4tuOO30rY0AhrDnGVPAFSQ8u3EzXuZaIOnsN5HUAJNdrM1zP//omgbeEHasdWuqXVA+KzUhX1QEQbWdMGkAylebgIO9B1rgnOcg/vkn7LvgmUvIcsL4P//sOu1QYB2gCgHrt+nKM230uOPyfq4a5Qw44/fXvByAjuNKTrmfVaSAvwgjhmSkkUamky6ekrPPxuRLf4mPD/g+Rq34I4rLf5G3730JznyuTrHfkpyU/RdywhA5CIKABQsW4Ouvv8Ydd9yBAQMG4JZbbsHYsWOxefPmnPYXXnghFi5ciNWrV+Odd97B7NmzO7We008/HbNmzcJtt92Gu+66CyOM4qKNjY3YsGED5s6di0gkYv27+eabsWrVKtcyJk+evOcbTBAEQRBEDlY6MkOEEbrwUpZOds11kLcfRsaI5VX/Ad+D6ciKghIen3MwfnrciLztnC94mZT5wrpvPlKbgxU/XGePTPRywpj1gDKiMRLUHGFa4JBRgu4gSMgnIA0AwajphOHAm7FXv4C0ETR1OmGC6cLCYMXlP7b+FmX/tE97A0HkMeKQaoSLCvRDEnMCMkIwAMFIA2yip7vHnUZ0naCj5ouZ4s+sCePnhKkaUgTRuAa3qCygm88F09c4cVyN9bfUjddHs+ZKa1vKTkfmcx0wryfZ6ci8rhvRIqPeGC8hHBexaOOb4DWjvVf/fbZJ8nDZOJGHDc37OdH3Mc/ZVJy5pwQNuOlJ+xq8o2y89XdlQxTHX+R2pmkFjpFstn75Mc78x5nQd65FynBxxKVWIFwBGAK8orcYy85ywhgiTFIEjmtnLokWR6bNUV/rSBkpr+JpY1nJA3Hq+24nT3k7m3a6W1lNGDsd2fawnWqvqSPXHqRmzHSZ9r6a+ilzaKbiuU6YMN8MAAhoGoIRdg1s19KuWmeSJuMw8SOYY8uVENs4rwC1lM4SYTwcSaLDXaCbjp1drB8653DC8AJGp9II6/azkZIJopnnHU4f93Nq8XdOA285W2Tgq39B1zSHCOPuz+Bn/wTI7DfLiEE8WlSE9nrmkPUUYYYdw57TDSFFVgOWCOOVotRk/BHfZH0SA9B4ydi8zl+zU44Uiy6MB8jxtewhPmOIB1773fqKouSkjlUC/s9AlYPY/sgIQcf13b/vnCgi1roOR/3nKlQ2fpRbm6YvYxzTlhOGRJh+Sz866voJUoi5Unpjvd1MbW0tzj33XJx77rm4+eabMWLECDz88MO48cYbXe1mzpyJ733ve5g7dy5OOukklJV1buRgR0cHPvzwQwiCgJUrV1rzNUMtf/TRR3HooYe6viNkXWzCYcqDSBAEQRA9gelSSHSYTpjCL2XFVSE0be3AEacP77Z+jDy0Gste2QKNy1g5+j1SsO81OJ5j4oJuF27dV50wmjE60ay9wnPeqR4CIfbinhFD0GGn/SmUjswsPm5SUeZfhyUYYYHUkG5ne/fb72aqCzFjp0TxDFpkoYwaZU/0kxdcTvAQYTyCFnoX07ER3QfHcVBEHsmMhoQR6LScMD6pD3mewzHnjcbLjy6z5rXtSni27YvEHC43uRtFGF3gAFU3asLkr8kVDLrTJFrpyDzax0rsa8+xn8goGcaB1wVW1cHjWuDrhOHzu374qO1U4EL9KBUOYSGLPH717bF47ckPjTi7+3hqKrJrTkSb16Fy8gEoqQ7jTzcbzsRC9ZqyUHmgNdUKVROteiaHpjcCyhjASDMloxUdqIHOu2MjphMmKQEHJVN4NRzC8kH2+g9crSOejkMRFCTTLAYTTaloi7D6EyW7vsSukpEo62DpplxOGE23gonBZNqqCQMALR0ezouWBICY635V19iB7QNZ7UGnxKxqOor4DbjusQwGNQL6CZ8Ak6oAAFvVBEyJSFRlrA2J4IyeBELG9nvsYzlLhNE9nDAaZzzcgbluAECzBjCothPGEBKK9AQajVXJagC7BB6cVavKLXxU/OAHEJ8/ly1JDAB/OA044127oLzkvnbIgwYhPboZ2AmkxTD+FgxjUBX7/VOS9/2cEwSIagIZMQRJVSCYKeXyHHNFRTEAbJBKxogpduWZNvmFj6vLWKdZb1EwnkEsJ4woWqn2MmZqRo9+BiP+z4VRY2ACq2dTOB1Z9vL7kwhjiktmTRhywvRf9s1he70Jx7G0YHv7Xxdv5l2lpKQENTU1aG/PzSctCALOPfdcLFq0qFOpyEyuuOIK8DyPl156Cffddx9ee+01AEBVVRVqa2uxevVqDBs2zPVv8OA8hUkJgiAIgug25IDphOlcTRgAmH3jFMx7YBoOPKau+/ph1GqRVMVywmh87waUs/fFvirCTKpnqbDak+wYEH2CqXLIeJHleKiC/TLsTE/iRXYw1Kz74kXAdMJo9gtModRMTieMKgTytDRwBJgKFjbvI7B0ZO5RxKYRKTjhQHummj81HNGzmIfWyq0sENhSIB0ZwOoCXfbb6dZ5cchJ/ec9yOl+kbpYXyAfphOmvSNjiTB+EY2IkcJQNYSRfOlqAkHFqr9w2BcKHt36H6vYN+fVfz8nTJ5R3gAg1dn3RrG0NG9bou9y3mENGFdXDADQs0SYnaVMzC/f/gnUZuZQKR8YgZReY7Tv2vmwoYItv0mqBDgeGlI4u30jIAWtdPQKx9bTWnSESyjR4ka6LwkIaaY4wGGHoQW+P4JD3CjMbgrE49duRWuEyRzVW94FAERSxQikw66BFZquQzaWOfflJNK8LcI0teQ6YfS06YSx70VmUfhUIssJo+s455M3MYiVEUHzy/Yg3+2qvZ5gJoIbKsrAGakDZUPYFDwGBWc7YXQPwXTSIPt7+lv3su006tHo0CzxxaRa/8z6W1aDSHAcNJjBcfc9lw8GHTVeDNfwrvXW85LkEVRXGtixlJYjaNEEaBlzmT4iDMchI7J9EEmVQDTqWiGPE0YMKeA0dj+y69P4P//U3HKz72demDVhREOE0QUjRVrAfiZbe9p3XN8Z+eXT9vcj/s9uxTEmsGeEzqUjE4qK3DP6kQiT7YTJ5/gh+jb0yxE5PPLII7j00kvxyiuvYNWqVVi+fDmuvvpqLF++HCeddJLnd2666SY0NjbihBNO6NQ6/vGPf+Dxxx/HU089heOOOw4///nPcf7552PXLmbpnD9/Pm677Tbce++9WLFiBZYuXYoFCxbg7rvv7rbtJAiCIAjCHzPFlElnhYauFvQshKywF2VRkyEYqRU0vndTK2W7gvZVEcasCfPxhiYAgOQjeogSD0MfQ1oMOUSYrtVhEST/Y8fphBGMoJdvOjIDZ7AnoeSvrQIgO3tI/0AQcpwwnJGuT6qqQujggwH4O2ECBx7Qs/0jAFgZ8tBmCJp2TZj8QSCO4zD3riMx74FpOGB694nbPc3Bg22BQelGJwxv3F/a4oWdMDHjmpFdq8qrPcfzEIyAcHGcBf2qm8waMh7tfUYhFxJhnIEzTu4/6eWIXAYNZLVA2sM1rvmmKFPc9JWVLox94F3DaKvPremzoYdgdcNM7Iiw6c0KO//jwkbInM5EmEPnAYO+YaVwSkTGYN2n26xlqEZspS3AIazrmLMjg1Aqhv+MYZ1oLIIlwpjpyCqbOtARYq6ToubV0LEVAKsLozvuqRlVR2WCiT+8nsFB2+xjf9VKd3F11+Z7iTAe6ciO/HIbvOAcOzCUMusVG4H+EDvXvQZRSFm1RkwxwElAtoUZ9Ys3YHTO7Lh9ITc4EM9BzLCUYVImABUcdF2022chGgNDMsY1SdN0hwiTe50Mj5sKAEhLEQRTtvs6W+CxcFyXDlv7beuZmcuTwpeTZVf9PCC/8734tNNy0p16L5j9LyyzPoi6KcKwPkaPPdbjO+xLAzb/B+OWPYpDl9yIgOifjqzMcDB+HavFrgA7FvI9F8oNDVmr6z/P7pYTJm0InP1JQCJckAhD5HDIIYegra0N8+bNw9ixYzF16lS8++67eOGFFzB16lTP78iyjPLy8k5dyBobGzF37lzMnz8fkyZNAgDccMMNGDBgAObNmwcAuOiii/DYY49h4cKFGD9+PKZOnYqFCxeSE4YgCIIg9hKmA8Wkt+qeBI1CqcwJY4x2FKL5vtLjSLJ7X3S38NRXSBmV3luMAIng83LLcRw4Y59kxKCd9ocrXFfFTF13wPSBeduZNYokRwCmkPjVEaq0/o62+eQtd8D3wzS36fXrIadaXPOcP5M00NivPk6YQb//fU91jXBw5PAKAHYtmBZj5LdfTZhs+ts1prbYTiEjd2PfTREmHk9D0zonwmiCbKRJzJ+uxgxEZsxR6sZ1TNm13aMjPiJMJ4Rcs2h4bObMwo2JPsvI0Uw96QjXWEF1wHY5FDevgq46xW/d+K/7+PvbiazN9dt3uOZvqTsfaxu+iaI0qymjCWx9SWEXBB0sHX0gBlzwIjKwU9t9+QkTTXRdh7pzJwCgOQyEVA6hr27FeR/eBAgjAQC8BocTRkMoncDMD0RoggxeSyOY2IGK7VsAAIeu/xZ0R3F7TddRlEoYy0njqgV23bXMltwabJqRR9Y5aEAyBAw1rWNXusneh5oOQXefTFW72HSJQ0uxRBgzpVfQ3zWRnY7Mq0C87ohltS2PAG3bbMed031SxO6rvJ7GkNV/AwCEUwGoHKAbz6leNfEE4xqjikHoOpBMpazrjFRUk9NeD7J1pqQIqnfp0L364sAp8qaEhJWOjOP8nTCcILhcw0DhZytOsY/3gb99KG/bkDG4pqqDHYvhJnY9rfrltR4LNo4RAJXbP0Y4vg0lgaLcdgaS4bLRecmyABdySDuf83S9/4y8SX71FQCg/R3mTiMnTP+Ffrn9mDlz5qDJzL/oYOLEiXjyySexevVqJBIJbN++HYsXL3a5YKZNmwZd11Hso4Kfcsoprouac10VFRXYsmULfvGLX1ifi6KIJUuW4E9/+pM17+yzz8ZHH32EZDKJnTt3YvHixTj11FMBAA0NDdB1HRMmTNj9HUAQBEEQhC+764Tp9n6YwXdNBm+MMBSE3h1BHCt356jeV50wI6uNkYXG5uUrsB0068JIthOG4woHmA88pg7z7p+GI88Ykbed1z7O97ItlJSgqHm1NT1kzd8K9iV0yMEomjULlVddVbBtXyKYcAfvXO/mxj7SVXukcdnFF9sfK4WFMmLPMWu/fLh2FzKqhsbWpGv+vobiEF6kbrw+8ikWTNS3p6yApJ8Ioxh1aXROgMZLBdPVmKPUm40aBJpxIgmqh6PPYxEfDuMgdSKoN+gPT6L2nv9B+SUXF2xL9F2ixUGIaZaqPRFgzq/2UDVSCgsaB+PbXCKMVXCcc99HXxnErsHVx96CD6OH5awnlmaCSVxmqbJSfBOGptNAjZ1ushRfWX9vWcdUCq29w6of0RIC0vGR0DTWt4j8Q6xu+CZEvQL3/fc+tvy0imHxT9Aeqmb979gGDjqirV8DACrbByH96ZvWelRNh244v8yUmCmBnUOftzblbId5PvEOEUZQkyzNF4CPOj51LTubXz6fxuAtOr7xuf1ZUbLC0LaMczvkL8KImbjbNerxPHP4sArr7x2qBLx9n5XJS/cQPtQkbwkYciaADMdBh1m7MPciIQhsYaqg4NN4CKlUh/W8JMu594JBlawWT0YK4+fP6tBU437u44RRW1tRu3Ex6394IwTjmZnPZ/MVhBwnjFBg0JWesZ8notOne7YxHUtmOrJLlv6fex2RCORhQ/Ouh33f/3nfdE+L4Ow0tQXuN8Wnn25P9CMRRjPKQnQsWcJmkBOm39IvRJi1a9di7ty5GDx4MILBIIYOHYobbrgBqVRurkknuq5j/vz5GDBgAILBIKZNm4bly5fvpV4TBEEQBEH0X+SA+wFf6C0RxniBq5Zrkfr6bDYvT9qqvYES6hsuoZ6mxBBWOlIs+CDmOQYUo25FxpGOTC8wItEkXxoyE++UQP7LL/7Od1D39esYvvLPOOT9WxCKNxZcB8dxGHDrLSi78IKCbfsSZkoXE6czPbVmLQBg5/973JpnjsQn9h4Zo3ZCeyqD99bstOaXR/ZNEazMsV3VRZ2ox9RJOCNoJjc7nTDebU0BH2DuBK3ASOnmKBOd11SzkdKmaKOV5uaLUjyyM9w5i4ecp/aCiVhaitiMGZSOrJ8jC0Agwc7lhMJEmA0Dp1mfS5m4y4FoHrvtdd7pVGMDJkE+4bac+RFMB3QObQI7PhNiM7jjbgIOPAsAi3mN4P4PkVbm9mzbmISa0aDuYI6DhASkJA4tHQe7lru2YSbkyA14e/PbrF1axQ1v/QVtERb4L1PWAWCpoUwSGduxquq6VW+JN2qKNMmfAwDCAVsUMjH1j9YBEmAUoeegW+Hz1E77uUrVdGwuc58fVVt53L5AhZ51+pbEq+3i9mF7gMyA39zhasdBt9oB3uJtWcRep84B0HV0pNn9Ne3hJuF4+/4rq0FocDhhPK5L2m0/tP5+vmkAUskkNNNx5xFUb6isM/oiQBMCgClO+dXb0zQE42xQhpIJWwOX8qUjk6qrLQHahM9TE8b8TiG0JBtoUGTUcFxelvvckfpqlWta7+iAfOAE1zw5z/O1WbtGAMBbGmcBF0/AvjeJlVV52/ZlyAnTf+kXv9wXX3wBTdPwyCOPYPny5fif//kfPPzww7jmmmvyfu+OO+7A3XffjQceeADvv/8+qqurcdxxx6G1tTXv9wiCIAiCIPZ3cp0wvfPYKBqBtDKxHL84bjwAoLo4mO8rPY6k9A2Bqqcxa8KYiHle+kxhitWEMb7nUzdhd/AamZk37YQogIOOuo2LEGnfBD7auynsehIx3eGadv5M8f/+N6e9WSeGC/buebQ/MXkQC+S/vHwrLv79BwCAqCIiKHffOdKXKA3L+Oulh+M33zkAV88Y1X3LHc+C3RlNt+oj+ImxHM8hY5wLjRf+GNuGsfpHfiOlZY0FU4dsi4DT7YBtetjInLaRY47JmacKHKSShs5vDNGvCRTFLCfI5prDAbB0nABQspOJEY3334+2N033CDteMyEdw0/ZgujRR+Ltb9lF5uuidb5p7mYt/SlUkV1DEmIz8I0fQec4XPv8Unz7wf/gdW0SJv/XFh0e/sEibPuEOUG3xwDoHHa1HuW57LpdowEAybSKUEpHa7QeAFBbsgIAoKSaoeub2BaItqCqqjq0LCdMOM7OoaqUnpPqSTfUEz7TDN7j3jN8pd0/VdMRl32EhiwbWiRZBJ0z6o44nDCRI490tdv+0+vdi/F4fgiIglXEflUZDwRL0GTcX3UPESZWF7dEGEkLIAPbCeOVpT8zsg6cUSfo20tkpFJx6zoT8HDCCJJdqyotRa196FsTBoCcagIARJLFjnRk+R0fgZoK13QhN8mAO3+D8BFHYNCT/ulMU6uYwNJ0068AAJvD7Nq99RjvGtMmouIW3/KlszTTdAZ0u1JQob4Hx4+3vx/pfyloLcR989lhf6BfiDAzZszAggULcPzxx2PIkCE4+eSTceWVV+K5557z/Y6u67jnnntw7bXXYtasWRg3bhyeeOIJdHR04Omnn96LvScIgiAIguh/yNkiTIGRcT2FOZo50ZHBAGNEdW87YXL2zT4qwpSG3S/D+ZwwIcM1owqKIx1ZN9aC6GI6Mi6r6K62Dw/CynXC5G8vVVdh2OuvYfibb/Rgrwgn4wcWW3+3G84yRdq3gygHDSrB6ZPrUBzqPsdHcRWrfaGqesF0ZACgGdeNnaMPRUZm9w+/AQXFO5iTQBUCEFTbCcN7BEf96qDK4UrP+cS+R6wojFD7ZgBAY8UE6ABSMnOrDNjyDgAWiN5w8SUA7HRkOniIAQ0D61/GugY7OF6ilACCcaxlxcwr2+uRCA8DAASDLAC/fmcHnlqyHp9+3Yy/aFPB6xoibV9b33n7XdauNQhEkyXQdCZ8VElfupZd3zQajR2NiKfZdak1wtwXlZKdzjPazpalOdKEqXpuOrKkURRJ1gP4vy/t1GVsm4x0ssEgIkflCkKbYitdy1aSnRNhginF0wnDx2KudlJpqWvaS+9yOmEmLhegiwEIVnqx3P5wAiBzTKSR1ABeDwdhpkbzirQOrRgJQWX3a1VQkE4noRsd8XMES0Ytm7QUtkWYPDVeTNdvaUeNnY6Mzy/CREYPc00XGnSlDB6M+scetQZ05ENtbsbwyggEww1ayAEolpe5pgN5xAY5aD/nmfUCC9WEiRx9NGpuvRWD//eFvO36OpyPYEv0ffptIrnm5maUZl1InaxZswZbtmzB8ccfb81TFAVTp07F22+/je9973ue30smk0gm7REJLS2s0GU6nUY67c4Hm06noes6NE2DphW2HhPEnmKOKHEed7quI51OQ+jG0aYE0VnM62L29ZEg9kX2t+Odz3pKbNne0SvbzvHsGSvekkIybqyf793fQZDdL3kZNQNO27tCzN44Hmtj7uCjwHG+65MD7KXdKcL06O/Esf3uU58WXj/HvnrumsWNLQrs93Q6Da68HBoAbR/dJ33tej22OoyIIqItaaciEnv5OtYfCYeNQG5Gt96/Oc5/P6ZlDnJaR/v2DqvANsfpnu1N3VYVFAgarPRlnOB/3ctZBgTPtn3teCS6hyFr/47GykkAgJ2lY9BUzGqbSSl3Efh0Om05LHRTREg04fmaeqtNJpOx0pdxXkWHDCZV1CCdTuOtFdsAAGVhGT8bUwe8CUz+8HYsmno/ACAVN4rACxzGtLLAdqm4HrNKr8EzG+8E39yGHWXjMaLxYKza8jU6kgLSYgiJYDlbbn0RWuGuN6Zpqn0sZ1QohgNFDQKIA5y6CwCrj/LLJd/HzKFOJ6YhCISLUfrTn6Dl738HADSsfQlrG05EQmqzlq22tqJ2JzpFOBmyhAxdFn3PsaqYjPtjHfhhiyHUeFw3ZB7gdBU6x0PjRCxq/sp6ttOgudqbT0dShtXqkNUwFkZj+L4hfHgtPyYVQVSTyEgRZIQgEokO6Bxz6eq65tl3Od2GRLACKSkKKWPGKVXf7QwbwmBADQPGsiHyea89kkPM4DhAVTNQ/TSw3SAaEMEbxz8neF8jTUqvvBItL75kTSuC9/UaALzKQ6paBul0fhEpfNK3AOS5b/SH67WQ/zcl9i5d+S36pQizatUq3H///bjrrrt822zZsgUAUFXlzvNXVVWFdevW+X7vtttuw4033pgz/5VXXkEoFHLNE0UR1dXVaGtrK1ifhiC6EzOlXiqVQjwexxtvvMEe3Aiil3j11Vd7uwsEsdfYX473RKMAwH72Wf7fr7BZXbbX+8HyiLMXyU8+WA5AQWPjVrz4ov/zXE/TvEEGYOSV5nS89NJLedv3JD19PCq8gKQRhIh3tOHFF1/0bNe0SQEgGyIMC4gkUynf9rsFF4GdEF7Pu+ySlSvhTLChc1z39qWPUFdfj8D6Da557S27rG0d4fGdl/75T3fOsn2YvnS9PqiUx+LN9n4/rLRjnzwme5KdjTyAMCRNR2tzGxSI2Nm003c/tiCIMERsWLMRYlxEAAK2bt2CF1/ckNNWU1mAMyMokDKwArsbNn3teb+JnnkGap75k2vel2u/xIuN/r9pXzoeiT1nRMdW6+9PDvi+9bdipIQyefHFF+0i4D4uKtbGCFTr9nVi0LqXsW7QCdZ08661ePHFF/HGeh4Aj53tSWTWsWczXtdw4Cf345MDfwi0s5owGR6o6mAxsRJxA3hOwzGN87HjyyjemXIjECjDK8+9h+ZdPFpiDaz/wmYsKT4eVXiYrdTo+8aNm6xzbeVqDQcKRaxP1SKiO4H6ZAdaJaCyfRAA4L7/vQ/DJOay4DUBmgCkm1rxyrvvWvcm0+lx0EoZr/7zn4AgYNeSNT57PLfgfTgVsf5+bdFr4B1jR5z3v6Uff4QEPxKfyhkMTwtYq7Z4XjfSHAcREpYOieDDTR/jG7pZl0Vztf+28X8l0wToGnhORCgdhWmBSaa9n3+EDBPHthcpyHz1JTSuBgDw4X8/wPINucqHlGYiT1oKW8cHj6TnskcAELQ0eDUJTVBQnIghoQDito157zXm8xvbzvzPVp3Fue+3bN8Fweh7447t1vKHKgoExwB4AHjl/fdd3335n/mfr7lgGHrcPl/+9e9/eYozu0Nful5nP8u1trfT80MfoqOjo3Ajg14VYebPn+8peDh5//33MXnyZGt606ZNmDFjBk4//XRcdNFFBdeRbRXWdd3XPgwAv/jFL/DTn/7Umm5paUFdXR2OP/54xLIsjYlEAhs2bEAkEkEg0H0FBwnCD13X0draimg0Co7jkEgkEAwGcdRRR9ExSPQK6XQar776Ko477jhIUm66BoLYl9jfjvctq1vwfx98Yk3XN9ThqJnDe6UvC954G+mEirqaBny2ajMGDByAY2Z2X52BrvKJ/DWWrGJBAlESMHPmzL3eh711PN62fDG2tLCX5PKSIsycOcWz3fvaWny0bgP+PWQa1PZ1GAlACSqYOXNat/XlpXXLsOEzNtJWEPPv952bNmHnP1+2piuvvw7De+F36ml2fLUKux591DWvtCSGSca2fnX1z3O+c+KJJ4Lbxx3UffF6PUPTsXJbGzKajqAkYHB5KO97KZHLto1teOGDj6BoHDQpACCDysoKzJx5oGf7pZ8uAdpTULkoQrIKxFUMrKvFzJljctq+9KdPATAnzC8270K7ISYPGzEMJ84cmrvwmTPxVZYI82n6UyycuTCnaV88Hok9Z+H/voOy7Uuxo3y8a36oY5treubMmXjyuYcAOJwwWZj3s0Hrwvj9Bw9Z88t2LrdEGDHdgbKGMsycORNLX14BbFyLc6cMwkGxEDY/xVLuK8lmAEAHWA2Zgdt1rI6zQH/RqLHAFkA6/Nvgvvw3Krf9F+vrj0PZV8Mxec3f0VTEBJNieQW+ccJF+EdsCkZeMQdmfrQB5ZVWP9ct+LfVxwGD6oHPNqEsuRWtjsO7eEQxZo5m7R/7+7/Y9qxuxLSTTsJX11wLABA0Npg5lJYxddQohIcPR+vndq2R5NgBUJZv8txngFuEOWHGCVYKWwBYffMtVirSww+ZDPyzFS8H03g5mMbUARWYOXNSzvLuf+nvEAEUt0uo7liBANi5H9J11zOH1nw0+NWvQU9wENQkVDGIwTsPgKaz9SvBgOczytN/YdsWTQQglYTRtJ61P3TKIagdWZLT/u8vMCEsLUWggQkyGu/9/LPmzrugNjaC1zPQoCAlsponssDhuDzPP//l1+ODNUxolmSxW55pnc8e844dgy3vPA8AqB04EAcZy9/8r3+h/dV/ub43c+ZM67vh079bsC/Pf/YxGtfZ6WZPmHE85MCehbn74vU6+1kuVlzSK+8ehDdmBq3O0KsizA9+8AOceeaZeds0NDRYf2/atAnTp0/HYYcdht/97nd5v1ddXQ2AOWJqamqs+du2bctxxzhRFAWKouTMlyQp5wRUVRUcx4HnefD7yWgyonexbff2ccdxnOfxSRB7EzoGif2J/eV4D0Xcz0PltdFe2245ICKdUJGKs1GCkiT06m8QcNRKEUS+V/vS08fj1lZ7lKIs+u/3tp0skDJMVfB/pQ0Y2QHwfPfum1DUPiZ5nsu7bCGrHk3JSSdD2AfPW6/c7ZKIvPtGkmVw+8m7S1+7Xo+r80+nTRSmpJQFFQPgsCORQQkAOc/9oA06SgCEdM6qZS36tBeNugmqqOD45gSeM0QYWen8MXT+mPPzn3t97Hgk9ox/TJyJXy643iXCHPL+zVb9FxNJkqw6L7pPrTTzuJgw7HiMbm7DY4vYfKegU9K0Ap/XluI7koTWBHseigVlFE2bhs1Gm40lRjp9PgKVF1HemkEgyWJj5ZO/AYzbgBIhiK0LDkDd169hff1xAIA1g78FXmX38YHKUkhFNQgMEvH6wIkwZQ5pe5vVT7GtBTpY6rJoRTnaAQSN9GRJwahTxrHt0nUduvEZRta6zgHecKCpgoL16z7GAWPGQGlj27BkJIex82ZC+eFjjj2VVRMmHbX+VgKyVawdAMq/fxm2/fp21t9dOyHwMlSjro3k8+ymaTrAA+WtIqKqaqWG45F1X5Xs+jOqyP6WVBm6EWLlBO/ly8k4EGa1p6pWPQ+d/wab73OdkcH2T0qOoINnv48ueF/Dan51I76+9DLwOsuQoglskG7bIUfkve5Eiu3BvIJPv/eE8mgQjYYTRlJka/mcllurxrnuyIjhBfsSDLs/DwQV1zGwJ/Tl6zUnin22b/sjXfktevXpu7y8HKNGjcr7zxzdv3HjRkybNg2TJk3CggULCooegwcPRnV1tctClkqlsHjxYhx++OE9ul0EQRAEQRD9HedoQgAYN7W2l3oCyAHWl0Q7y7nLd9ML1u5i9gfwLhi/L3HKBPt3l/MUa61qsB3jZqtCBVK7SsDxsl1ov0ePOdo1zQf3H8dwSKQ0ycS+iRKyx5CKaRbAE30KWgNAwwAjQJvUrCC44HPtCIosSKjyCtq2BKCZxb7z3G8CY8e6pg+pOST/BhD7FMeNG4CQIyVZya4vEGnfnNPuiwkTEe4warvmqfdisnZ7nfW3nG5F8a4V4DQVVVvexMxRs7Biayv+9AFLqaeIPDiex29mseN0xMZ2JAWWGqcjVA0VgJwYAAAorQkDgRg4SQIfjUJJtaBm03+sdWlGHqdh8ieAICIoi1gwZiaskyftSH+esOveKOUs+WcETCTlDTfIk8sWsK9t22al95MOc58zgmrUgeEl8PcvBACILU0AgOYQIBdVGvuNsbqa7T8zjVkw43j2yDq3ecfgaj2ZREiyn91En3iiEGDboPES/iTHrBSoOudf3H7g14sAALIaRDjBG33xXr5opCNTxQBUwLrO+A2MCBiPPWkpAt5MU8d790UoKgYApKQi13y+gDOkqNJOPZxKdE+K++IzzwAAxL75TYyuieGgrV8AyLqeFkqnrxeuux2IuIPf+/ozucm+7mbel+kXQ6A2bdqEadOmoa6uDnfeeScaGxuxZcsWq+6LyahRo/D888zmxnEcLr/8ctx66614/vnnsWzZMsyZMwehUAhnn312b2wGQRAEQRBEvyHbzt9dI8t2B1MQMkUYvyDa3sK5b/xetPcVioL2C66U5xioGcZe+ls4zXrB4Lr5d1LCjgBsnsArACjDhyMwxk45tK++sBafdhoAgNfsoqDKpi/zf4lSYBH9FEHkkTJO5bARvxPzXIMlU7RJaVZdC797mQQjGCzIaPwkZjkWuiLCTKycWHAbiH2HgCRg7rE/xyHv34xxy36HAz99yLOdnkggIzBBYAt/AZZ1nABN9z6udmxsw7+eb3XNm/Dp/TjyPz9D5Y4vcFD1ZCz9utn67PBhzI3SGmLXdQ5AGusBAC3RQfhi8CDwWgg8DxRV2u6NukdYmqvRK57GAVn9Lr/iDQBASBbQGCpBWjAEJEdMPG2KT3oGSiXLPCN/wgLtkiZjyI4J2JFqxrKNzfjC0d+OXQks29gMvZp9x0xH1lI0BEsqLsKD817DptRUvDbtQYhFD+Ctx2vx2rQH8fq0B/HatAehCEcCAESVuW0k1fDp6GpuekfRfmbQkwlEHM9ufs+RHMeeeVojA3HsRzokwwysZzcPstRhYigDMcNELzkTtEUVH9FGUJkIkxECSIK3auj5iQcBmR0naSmCohbDZeMjwvChoOd8UcovwhQ7jgtN9RebuoIyxEjhqGuoKwmiKt4EAIgu/dBqw4fD+ReiF+6LU4QRRH6/SfEZ//jj3u4CsZv0i7fGV155BV999RVee+01DBw4EDU1NdY/J19++SWam+0L/FVXXYXLL78cl112GSZPnoyNGzfilVdeQTQazV4F0cMsXLgQxcXFvd0NgiAIgiA6iRToO0FryXhxTrT1DSeMc98I4r79whd1BC3yOWGUEHsRDuicNUizu0ckOt1Zglz4+Ky86mfduv6+iFzHRkyLRvFeAEh89FFvdYcgepyEwq4rEY39P59IIocc9w4jgOwXfBWNa4pmBMvN4Gg+8bnyqp+h7JJLUP/cX7Dk7CWIyhRn2J+YUFeMzZFy/HbqGajc/gl4Pbewuonzs8Ut8/BKyw9dn3/+9iY8OO81PHPTe9a8+vWvGN/VIKp2atBkhh3Mx4+pwkGDmBiQcsTZx65eCwBoiQ1CLD0EADBwdBlEhxPEOUihbOdn1t+ril8HipgDNmTeZ81TxhEU5xNGyjE9DTFYzJo5tvH4FRegKF6Jb92/GJcteMea/8TXGr51/1t4RWb3LsGxXYlgOTqLkmD14SQtmLNuk6KTTrL+Dk6YgFjADtiLPk5dU3TJiCFUNekQM8b5n9382PlAw5HIdIiWu0VWg5YLqKPe2z1uijCqoCAOrqAIo0hsu9JSxGrrk9EOfIg5WopaVrvny/lFmHBRbjmGPUZkfY0vX+4SyrXtjdbfhZ7R9E6IMMGoMz3wvv08Tuwb9AsRZs6cOSyPpMc/J7quY86cOdY0x3GYP38+Nm/ejEQigcWLF2PcuHF7ufd9C47j8v4z99/rr7+O6dOno7S0FKFQCMOHD8f555+PjGEZXLRoETiOQ0lJCRKJhGsd7733nrU8kzPOOAMrVqzYa9tJEARBEMSe0ZvOl2zM9F/tzWzEZG+/aDmdMGInxID+zOQGu4aFnGe/m2mCZHAwwxxCN6cjc+7rQk4YAAhPmYLae+/FoKf+0K396Isc/OEdKN/+KSZ+9D8F2+4vI0WJfZNM1nVFzHNdChkBugjHgzNiB37uxdoLz2XLN0UYI31SvmuNEImg8qc/QXjMWISkkG87Yt+kKMjue6/JAzH8nbdRdNos37YNa19yTa+KT0NVawMAQFQlvPb7L3K+U731fc9ltSdZTEZxiCpnHn+F9XeshRVZb4k2oD3E6iFX1EfghA8E0HxMMQCAg47pi76PqW/8GNsbnrHaRBXzbm46YWwrjJTYyb6rZwCFLVvMxF3rKIlXorzqc1SH7Ljd+sHjUR0L4B9TTgUARNo2IBDf7rmdGa4NI6cVQwm4z8HSHcutFGCCbp53uamr+EAAwxYvwqCn/oDggQeisc0WfBSfZ9xUrWJsl4oJq3XrOqBlP89EKoA5f2fbbbhyZDUAzWjP+7hPTMEmIwaQ1Hlr+b4ijJEmMeUQYXifax4fZIJU8a6V7nUq+Z9TuW5+VgOA1FerAADpdetd83nFTg0rVVeDC9jT4gD3IHt0wpQTK3fUs+nEcyFB9Db5JVFin2PzZjtH6Z/+9Cdcf/31+PJLO2VBMBjE8uXLceKJJ+JHP/oR7r//fgSDQaxcuRJ/+ctfrMLwJtFoFM8//zzOOussa97jjz+O+vp6rF+/3rXcYNDbHtldpNNpKk5FEARBEPsgoRgLpOlmQdUC+a17GqcTJrt2zr7GxPpi628uTy57JSiykaI6EDRGqAvdnKrNKb6Zx0QhYicc36196KsoqWYcsOyR3u4GQfQ4lcUBZNo6rOl8Ismgmhi2AJBVHSkjyCn5BDBD9TUANiAZKIXG8Z1ywhD7N8Uh+z7UrkQw4JZb0PHuEqQ3bsxt27Ia0xb/EP97zIUoyrC0dacu+wkWDfkjDlt3iqvtlFOGYOyRtVhz0CbP9b69iokWkiN4ftakC/A57gAAxFrXsj6Fa2BGskuqc1M/1fzir7it8hgc+R6HjWXAk8cAR3XYke+YITJZwXDHIOiJ767AB5MBQc8AAmvH6yokIY20ymIyciaImVOa8Z30OLz5LPve/10+A3yEOV7S3z8cX02disOX3GCshsOI/7yBld9gKcduPoPHnFPexrFnFkFrbwcXCiF1RTlWL6pEc2wwWycM0YTzrh8iVVVBqmJC1DGjKvHnD78GACii97MbH5EAJKHxMjaWAVEzbZzP4IWi02Zh2xtrjO0NwBznLkjeyzedP6oQgLxRglbN2vk9LwVkts/TUriga8Z0wkgOZ2y+vjgZP30glr7+NYKdfLYqRGrtWuvv+NJl1t9O0QUABtxxOzb+6McAgOGvvQYACB91JNrfeBOxb84suB4l6E5HRhB9HTpKuxld19GR7tjr/zpj1QOA6upq619RURE4jsuZ9+qrr6KmpgZ33HEHxo0bh6FDh2LGjBl47LHHIMvui/L555+Pxx9/3JqOx+N45plncP7557vaOdOR6bqOY489FjNmzLD63dTUhPr6elx77bXWdxYsWIDRo0cjEAhg1KhReOghO1fp2rVrwXEcnn32WUybNg2BQAD33HMPqqurceutt1rtlixZAlmW8corr3TuByQIgiAIos+RLXRES3ogdUIXUIK2CBQp7t2+9DTO0aJ6nmGJHM9Z+yVkJE/v7nRkZbX2SF4a8dhJyPVC7GNIWee+5BNMBYBIjAXoeHAQM0ZNGJ9gp9PhmFRKoJk1YehaQ/hQHrHv/2t2GIFvnwLrAEsr9nXwcfxxwi3WvGmrz4Ki2oNVL7r7SBw0owGBsP/g0pgRePZzNSqpFgQSOwCOR3tkIACgpDrXqVVfW4Nrha349XcFPHmMITo64krRQLYTxv6uxrPPpHTGVdsjGrHPR0UNoWbHOqTa21h/tQx42TEwN6vwOgcd6S8/saYbizgEJdaeD4fBcRzkCHMB8WrK9V0pz363+uM4l/2cMIpxHVAFBcEUrOsA5xNvCx92uOUAUjIhSygRfAYHi0Y6spQUQdlXUkFhRRbZ9qpiEBmRCRh+7j/zd5DSbe51KoUHKk85eQgOOWkwzrjm4IJtO4Xj92j/z1v2bMX9zByZOhViTQ3Chx9uzat75BGM/PgjSJWVBVdjurABoG1XMk/L/k3Z977X210guglywnQz8Uwchz596F5f75Kzl3SbBbq6uhqbN2/GG2+8gaOOOipv23PPPRe/+c1vsH79etTX1+Ovf/0rGhoaMGnSJN/vcByHJ554AuPHj8d9992HH//4x5g3bx6qqqowf/58AMCjjz6KG264AQ888AAmTpyIjz76CBdffDHC4bBL4Ln66qtx1113YcGCBVAUBWPHjsUpp5yC448/HqNGjcI555yDyy67DMcfv3+MgiQIgiCI7uRbPzwQ/174GY4+b3Sv9iOVcOf67m0njDMH9WGzhvViT3oeZx2YcIG84kpYQrIjY4kw3e2EcVJcQal/OgXPA6p/nQKC6G9wHVn3A9n/OhMLy8hAhwgOolUTxrt9tNQeoZ0Rg+SEITpFXWkQG3bG8c9lWzChrhhFp56C7ffd79s+lACag9uwK7AVJYmqnM/N+moAMORv/4fVJ51sTbePZzGW5jirj3fokFLXd8XyYmS2NwEAAvHtSATKrM+KK3PvmRzH4ZnADwE8Z81LxAZYfws8h6giAmaRec0WIjTedMmkEZw40V5oyy4AbL1yJoDm9f9BuvwbACoAXQVE+zzj5FzXxbrvXQZzrHhjKSDxbgGBO/3/Ac9e56olw/qas6i8KD7iaiAgoh3A9vLxOPJLYM0gs1aUt9irp1JWTbZQOgRwTGTxE2GCU6cDm4GdZWPZ9wuIMOG6GvCbUtAEGQmF/d58njS4gXHjIG12O2GUcOFnZjko4uBvDi7YrtM4XFpJR1mCbCcMrygY9uorgGP/chyX084PuZffB/YW5Zddih2PkNt5X4CeKIgcTj/9dJx11lmYOnUqampqcOqpp+KBBx5AS0tLTtvKykqceOKJWLhwIQCWiuzCCy8suI7a2lo88sgjuPrqq3HNNdfgb3/7G5566ikrndhNN92Eu+66C7NmzcLgwYMxa9Ys/OQnP8EjWReeyy+/3GozYMAAzJw5ExdffDFmz56NefPmIRAI4Ne//vWe7xSCIAiC2A8ZNLYMF9xxBBrGd75Yak8wbJJ7NJwzHVhvMe/BabjgjiNQVNGz6VZ7G47jcNus8Tj5wAH43tSheduaI3fDRrBT7uYR5KU19mjbKacM6dZl77N0YnQwQfQnOra6604oeYJwA4qDyGTFNvPVkBFSrM6FU4SR8zhtCKLESEm2bGMzAKD8kksgD/O/V5pH3wvj7sHXRXZa+EipgjOvP8TVVhk+3DWtCuwe+/4adpwWBd2B/upf2RlBgkbNFhM56H2eNA/7jmt6fdCdtiwWlOxsZC4Rhq07FdFZ0NxIhaXv2GavUw1il8AjbTphoAK8o7ZbaSlqbr0V24+0xRg9bd+zijQPt8/476B6/g0Qs0SYzpQ1CSv2Pgj4nNeKsaC0xJy3uuGESYS8RQE9k7bcLaIWgA7DCeMzaCVZbgsdLcWaoyaM97269LxzEUiy3zIeZM/ivOJ/zePD4RwnTDTWOUGjO+Ecv3PLi3Y9JE7JFd44UdztWnWByP5RjoAT9w+xaX+AfsluJigGseTsJb2y3u5CEAQsWLAAN998M1577TW8++67uOWWW3D77bfjvffeQ02Nu2DWhRdeiB//+Mc455xz8M477+DPf/4z3nzzzYLrOf300/H888/jtttuw29/+1uMGDECANDY2IgNGzZg7ty5uPjii632mUwGRUVFrmVMnjw5Z7l33nknxo0bh2effRYffPABAp1U0QmCIAiCyKUvFPEWskY6y3leQPcWgsB3ui5Jf+esQ+px1iH1BdsFjZfhsOGECXZzvRxRFvC9+6aCF3nwPVBIdl+E4/nO1LYliH5D3egSbPh8lzUdKCDKqzwAh3lGzONs4cIKkAbSYsgKjuZz2hDE9JGV+PTrZnz6dRMAFiwd8txz+OKAAz3bm6aSpNSBv495COVqDV499yWInajboRoSTnuKHdDRrGchPmCnegp1bLH+rh1Z7LvMbx0wAP/rCN2MKz/A9XksKIEz7iKc427SboifZhf0DlanidczVhtJlfFiJIzDd7AaNpye68osnnUqKlf8D7Q3G3M+C+re515w0qQcJwynpj3bOhlRGbX+9nPClNZFsfMduy+mCKMJ3s97ejIFwUiNJmoyYDroPFw+AHDg5Ag+X8qE5OaY7U7yrfMiy5CTLegIVSOplAAAuIC/8JBavRpS1q6IhHthsJDPABCO697raTC6f4gwNKBm34F+yW6G4ziEpNBe/9cTAZLa2lqce+65ePDBB/HZZ58hkUjg4Ycfzmk3c+ZMJBIJzJ07FyeddBLKyso8lpZLR0cHPvzwQwiCgJUrV1rzNY0Nn3z00Ufx8ccfW/+WLVuGd99917WMcDi3wNzq1auxadMmaJqGdevWdWWTCYIgCILog2Snj+kLThgiF3NEYrERhCiJdL9IJcoCCTAFkAcNsif6gIhKEN3J6G8McE0reQKSAKBnXS/yFW/Wytl7bEYMWcHX7nb0EfsWRw5n7oSWRAZ//mADAO80WyZCVv347cLmTgkwAKDqQDJjCxljBsRcn5uF2QGguPkr6+8Jx/oPojikwZ3S7KeTf+qaLgqKVj04XQOWbF6C7/7tu+CN8dw8zzbIrEeyo2y89d2SNiYKJf/1EQBAVG2Bxslr5WfjH1O1nPmST7hSiETAaylwmr0v9NZmny20cdeE8d7nRUWsz3KSLc90xOm893VGTyUtQYgDD9UQa+SA9zEQKpLAa4ZKkrIHGOeroScbzhbVSOXGRaK+bTONjblOmMjeH5TMR3LjdAAQm3lit66H4zgMO4i55cvrIgVa9184jnMJMUWnntqLvSH2BHqiIDpFSUkJampq0N7envOZIAg499xzsWjRok6lIjO54oorwPM8XnrpJdx333147bXXAABVVVWora3F6tWrMWzYMNe/wYPz56lMpVKYPXs2zjjjDNx8882YO3cutm7d2rWNJQiCIAiiT6Gp7pdzqZsdFkT3YKU7UVnAxi+9BtGzVF59lfV3yMM1ThD9mWBW+pnSIsWnJUPPSj8m57kucYbAn5FC0KgmDNEJhldGIRhC31V//RTNHSzAvr7eu5ZetgjTFTI68MXmVms6miVAKmPG2J+1brD/LvUPwmcPaqgOV7um06rtfvmgdR0ueuUifL7zc3A6W7eisZT10RNnAACKm+zBtePXszb1jezZwCmaOPmvdBD+MyhX5BB4737zkQg4uAvQ+y3bSVnYvlaU+QwSKTLmm+nWTBFG5X2cMKkUeC1lTWdEJoQpAe/rEsfzVg2ZSNwWU/KJMO2D3dlg5JC3wAEAVddcAzGTsKbLdixDMJj/GtkTlM+7NGceH4shOmNGt69r6tkj8Y3vDMO3fuDtPttX4Bx1c+T6ul7sCbEn0BMFkcMjjzyCSy+9FK+88gpWrVqF5cuX4+qrr8by5ctx0kkneX7npptuQmNjI0444YROreMf//gHHn/8cTz11FM47rjj8POf/xznn38+du1i1vL58+fjtttuw7333osVK1Zg6dKlWLBgAe6+++68y7322mvR3NyM++67D1dddRVGjx6NuXPndm0HEARBEATRp+CyggQyOWH6JNniGDlWegfOURC46udX92JPCKL7CUbdwdCB5bkFx51wWSJKPidMRSlbVloMWcFXkZwwRB6KQhIeO5+J3boOtCaZCPPbk3/i2X5PRJhd8Qya4mz5HAdL/DHhZRnSAOYU43UV45c+jLcHPY+y2vwOAYHzf6aqLw1ByDAhJtzOOs9pOjjDCZMJsRRZVVdeCQAYt/wx67sZI2W+mdrPKx0ZAAwuD2GXVxdFb8cHHw4DggApYw8QFnxqtjg5dHAp7vjOAbjl1HE4bkyVZ5tY2BRh2PaZjjjdJx2ZlkyC1zVwprvFaB/wET70dBpShqVuS8m2kynfoJXq0eNc0+GAvwgTPmwKOOgQMizl2dDV/wte2vspu8TSkpx5oYMP7pEMPoGwhAnH1iNcQJDv9zhEGHRzWjdi70G/HJHDIYccgra2NsybNw9jx47F1KlT8e677+KFF17A1KlTPb8jyzLKy8s7dVFtbGzE3LlzMX/+fEyaNAkAcMMNN2DAgAGYN28eAOCiiy7CY489hoULF2L8+PGYOnUqFi5cmNcJs2jRItxzzz148sknEYvFwPM8nnzySbz11lv47W9/uxt7giAIgiCIvkDVYHfKDXLC9E1IhOkdxMpK17RztCTvkbqXIPoz2YWYgwXSHnJZIooo+V+XYkadr4xDhBEEut8Q+Zk+shJFQXZcJtJMqGhJeAsOgrr7VbpUQUCLIcJkpxEzGfrvf1l/V+xYik8HLCq43MVnLMYNh92AD8/5MOezQWUhyIYIM+UL9n85Y4sUarAYACAUs//L6TaM/PIpAEBSLoag6parjPcRYWqKAmjOulV9XQZogo+bRBAgDay1BBIAkKsrPds64XkO351ch9mHDvJPR2aIvJogQ4ctwvA+14GSs88GAKsujIlfXWIhGoWYZiJMWmYikw4t7/NSLOJWqCIBf+HZTEl31FtXYtriHyLSvglccO/XhNG1XLWR6wUxaJ/CGWulVLP9lt6vakr0GnPmzMGcOXNy5k+cOBFPPvlk3u9OmzYNuu7/AHHKKae4Pneuq6KiAlu2bHG1F0URS5Yscc07++yzcbZxU8umoaEhZ/3Tpk1DOu2uQlZfX4+mpqa820IQBEEQRN+G4ziEYjI6WthLLqW56ptkizBcnvQaRPcx8MEHsPb079ozeMfvkOd5nSD6I4Fw1wJ5gpJVUyyPqBKxRJigNXo/X5oggjBRDIfVsXcvxrjaGNbv7PBsJ+6BE+alKaci8hlLtW6KPtlwHIcMz9ZzxdzOCYhFShG+M+I7np8FJAH2GcBBzOgobwEyRn0S3iEIDH7+Oaw5dRaKWtYAYOdRMGmn9PJLGaaIPDpaDgfwpjVvwXE8Vqf96/um162HWmMLsEI3naeybO8zjRetvvO8976UKishDxoEQU0iI9lKUjDkLXzI9fW2E0YyxZX892lnnwAgovg7m3hDsOEAcLpRryeU3y3YEwhFRTnzSITZM/R43J6gQU79FnqDJQiCIAiCIPo8pgBD9F3ICdM7iJXutCqcQ6TsjRGwBNGTONOJNRxQXrC9pLjHneZLLxaLspH3aUcwlUQYojM0tiWtv5dtbEFG0/GT434GyG6nFt9FEab23nuRnnQIzjzxBqyTitCSYINO42n/Gijn/EzA+T8RsKFyz49dWeCRKjPPGQ6RBFDcrkMV2L2Fc4gNgdGjET3uOIhGKqyMGEAwZacj83PCFAUlZNpGdblvDetetv7urscNQbavDxov2U6YPOmfUuvWQVCTrnmhPOnRzP2TNkUYLv9BESpz24TCir+o4il+yPndgj0B5yF2kwjTfXA8hfL7K/TLEQRBEARBEASxx8gBd7CTgpd7B07OCmw4gh9iiTsve/F3vwuC2FeIlBSuAZBdQ0wS/a9LgRC7hlnBUdB1jOgc3z3ILpQ9fWQFFl5wMB698UyEJk50tct2wkyomJB3ubETjodw531oVqJY1diOVIYt4JQJtb7fmVB9EOIBdtzeO/3eLmxFLkeNqEDCTAvGAdE4EInbThguy2VRe8//YPifnwYA6LyESEK00pGhxp1a1mTyoBK2cAc7oxwq5BrffgmlpZDTrda0ynVPkh+e5yxfChNhOpeWMDsdWcTHCQPYIozphOH4/E6YUIlbhFECXRMzeqIOy+6Q86xC7D5UE6bfQr8cQRAEQRAEQRB7jBJ2B0E4csLsFbJHl3qNQDUpv3ReT3eHIPYaJdWF0+yYwoqJLPsHa+WgKcKQE4boGocPK7P+DkgCpo2sRF1pCMiqjfH3Q9whuPEV4wsuu77UPs63NCcAAGHF/zi+c+qduOSAS/DKaa/g6PqjO9V/PwISjy06qz+jg0OsXUc0DqiGMCNk9YMTBISGNVjTF65RoRv1YwQkfNehdjTgtllM2Fk8jsPGcg4zauf69it6/HHgHM6a5rbuOU85joNm/EQrGkRHTRj//c3Jco4TJqj4Cw5ixqwJ0zknTDDqdrLIwf5Zp4pX/N1BRBfpI8Ia0XVIhCEIgiAIgiD6PMfOGQ0AqBtdUqAl0Vtk12qgdGR7B07MCg5l5a53pSKhFBbEPsDpv5iMQ08egrFH+rsBTAIh+7qUgQ5J9D8HBCNVmeooCE7XMaIzfNvhTJk+yi4Sr+vuAPuOmPt4umT8JQWXHXU4H1ZvbwcAKHnS6lWEKvDDiT9ETcTfSdJZFFFAG0xXB4fpjUn8oOx4W4SRc/vB8xxEjYkSdUtKLSdMwGf0vizwgC7jDf0GnPFzAQ+exNpLgr+QwQeCiBm1ZwAg3pr2bdtVBOMnazs4hndGGQKST00YANBTKQiaW4TJruPixE5HFgUAqHr+vgej7v1gisV9neCBB7pnkHDQbdAgp/4LPYUTBEEQBEEQfZ6RU2rw/YePxsk/nli4MdEr5IgwNIJ8r5DrhHG/4g246077MxJhiH2AykExTJ7Z4KoP40dpkS2oiOAg5rkumfViMg4RhoJdRGf55gE1qIgqmDGu2p6ZlWlq6flL8eSJT1rTYdmdasqPSJbjpCS0d+p8lIZlexM4DrNG/AgRqRoZgbkaRMVbbBBTbQCAtBiyU3pFK73bmvcsXYLuCNSLgv/5HTrkEMjp9i5sSdfZ3j7bSvvE5+kLgBwnTL5rk6Cx1GVWSjctk3fZuU6Y/CJM6JBDrL/D3/hG3rY9Sd3vHnFNN//tb73Uk30QSkfWb6FfjiAIgiAIgiCIPSYQcYsBYp6RoET3kZN+LGs6MHKkPUEiDLGfUZoVrBbznANm4FTn2bVMQ9+pp0D0fR44ayLe/vnRiDlrdqi5xegzjqC72MlaJoEs58uAor2T2kngOUSNem86gO0P/RZtixZBFZlQ6XefF9Ms5VZGCkE3XCR8nsLso4uZ/WRweII1TxH8habI9Gkou/giHLD0twjoHbjwziM6u0mFMdw9uxQRvG6KMPmvA9kiTD4HnSm6mG4irYATRlIE8I5jRikgwiij7Ht+xeWX523bk/Bht8Cop1I+LYmuk7+OENF36R8+NoIgCIIgCIIg+jSSLEAQeahG4WAxT7oUoifJEywiEYbYz9Di7lHmQp7gaHZAWSf9hegCHMdBygrWR6ZNRfzjjwEAyhiWVjUgBlzf6Qzb29wBbEXce4McdJEHoCIjsqC61tEBtZIJCHLAux9Smjlh4oEyq66KVF7quw4zq9nXHV9Y88aWHOTbnuM4VF5xBcoubsERkUi3ujwj44rR9t+dSKk6RN0QkPJcN4KTJiGTtAUHTlfz/q7RgycBrYDKs32YFnKFumyUiIx4B3u2KuSE4QNB6+/AuLEFl91TZKdKzUmdSuw2err70u8Rexd6CicIgiAIgiAIolswBRiAnDC9RXY6MtdnNKqf2M84YHqd9fcnciYnSO4kVhZwaZganS7EHlJ24YWovPpqFJ91Jup++zAAYGzZWJwx8gxcOfnKTi9nxthq13S+mjDdjRpg60rJMQBAZts2Kx2Z5JOOrGrycABAIlAK3XD7CHn6bGbvyui2aBrI45wxEWKxbk+zGYwwB06qXQbXCSeMWFaGpFJsTXNaflGl9MzvAoDlEBKihVPSmQIMkD/VGQCUnnsOAmPHouq6X/ape75YXV24EdEpoiec0NtdIHYTkiIJgiAIgiAIguh2iiqChRsR3Y48ZIh7hu5IW0FOGGI/w1mr6l0lk9cJw/EclJCIZLuRLohSvhB7CCfLKLtgjnsex+GXU37ZpeXEstwPAWnvDXLoMM6DtBSy5mlGqjA54B1SLBpRD6zehJQcg5hhqcny1VVJGrrFIcErsCR+O4D8qQN7kvKSABoBBNUihA1VNp8TBhyHYauew38n/pRN6vlFGCXkFpcksbDY1BXEigoM/utfunWZ3UHllVf0dhf2GcTy8t7uArGb0FM40S+ZNm0aLi+Q33LhwoUoLi7eK/1paGjAPffcs1fWRRAEQRAE0R8orgoVbkR0O9mjgnXNHkFLIgyxP7L1xEr8piiOFkGHVKDAtjkKHmA1YQiiLxCSbbHjgIFFe3XdQ2ujAIC0aN/TVaNukuLjhAmVMXdHUi6ynTB53CRDY0zoienjIG+/AO2rfwixQB2WnqK0mLl8ZBUI6CzYnU9AkocOgZLYZU2rBe6z2QNUooFIwT5NOYUNrhhxSFXBtn2JgQ/cb/0tFBX3Xkf2MfjA3qkJRXQ/9BS+nzJnzhxwHId58+blfHbZZZeB4zjMmTPHanvKKaf4LquhoQEcx4HjOIRCIYwbNw6PPPKI9fnChQvBcRxGjx6d891nn30WHMehoaFhj7bHSwQ544wzsGLFij1abmd5//33cckll3S6/d4UiAiCIAiCIHoDvzQlRM8hVOQfHdmXUpMQxN5i9qGDcMTwcvzo6GEFHQRKyA52F67UQBB7B8WRgioW6F7nRCGCEba+jMSEFR0cdEOEkXzSjkbLDREmUAKdM+qq5BEyzExlH6zbBb1jPLRkbUHBtKcIGUKsogO6xsShfAJS6ezZkDLt9gyj1osf2TVdOiPCTDp+EE67+iBMP3dUwbZ9icgxx9gTasa/IUHsJ5AIsx9TV1eHZ555BvF43JqXSCTwxz/+EfX19V1a1q9+9Sts3rwZn376KU455RTMmzcPf/rTn6zPw+Ewtm3bhnfeecf1vccff7zL6+oswWAQlZWVPbJsk1SKFcirqKhAKESjPQmCIAiC2L+hFGS9Cyd4pIZxZlQSSBgj9j9GVkfxh4sOxU+PH1mwrVOEEQvUXiCIvcWYATHrb3kvH5dhQ5TYHGNxG423zxE/J0y0lI3UTygl0IzaJ7zoL2SYi1m/swNtCRasz5c6sCeJxAyXj86hPcn6kq8mDCdJENRkp5cvB9z7LJ84Za2D51A9uAjiXkxD1x1wHIfYySchOHEiAuPG9XZ39gmKvn1yb3eB2APoqaKb0XUdWkfHXv+n613PVztp0iTU19fjueees+Y999xzqKurw8SJE7u0rGg0iurqagwbNgw333wzhg8fjhdeeMH6XBRFnH322Xj88ceteV9//TUWLVqEs88+27UsL+fN5ZdfjmnTpnmue9q0aVi3bh1+8pOfWI4cwO02+fLLL8FxHL744gvXd++++240NDRA13Woqoq5c+di8ODBCAaDGDlyJO69917Pvt12220YMGAARowYASDXiXP33Xdj/PjxCIfDqKurw2WXXYa2tjYAwKJFi3DBBRegubnZ6u/8+fMBMFHnqquuQm1tLcLhMA499FAsWrTIb7cTBEEQBEH0KWbfOAVHnzcKF9xxRG93Zb+EKySyUDoygsiLs15DqSM1GUH0Jk4njLyXHSITh5YCAIK8ApWXXSKM7CPCBAz3jCYoUAXmDMnnJhlfYsezUipLBLi3t9MkGmX9VXQAKutXPhGGj8XQFbmIF3iIvJ3sMN9+2ReoveMONPzx6cLPJ0Sn2J3YL9F38K6iRew2ejyOLycdtNfXO/K/H4LbDSfGBRdcgAULFmD27NkAmDPlwgsv3OPAfyAQQDqdds2bO3cujjrqKNx7770IhUJYuHAhZsyYgaqqPctr+dxzz+HAAw/EJZdcgosvvtizzciRI3HQQQfhqaeewk033WTNf/rpp3H22WeD4zhomoaBAwfi2WefRXl5Od5++21ccsklqKmpwXe/+13rO//+978Ri8Xw6quv+l4AeZ7Hfffdh4aGBqxZswaXXXYZrrrqKjz00EM4/PDDcc899+D666/Hl19+CQCIRJgF9YILLsDatWvxzDPPYMCAAXj++ecxY8YMLF26FMOHD9+j/UQQBEEQBNHTcDyH0YcP6O1u7L+IXkEO+3mV0pERRH4CDidM0KfoOEHsbRTHtX383q4JM7IUr5oTV90E7e5bAAAadEg+rhxnyq20xGId+RwfQREISjziaVuc2NuOHxPTDSeCgwQzHZl/X8z7alHzKjQXDe3UOgKKjjYjIU0+gYcgTAJjxiDx2WcozlMqguj70FCo/Zxzzz0Xb731FtauXYt169bhP//5D84555zdXl4mk8HChQuxdOlSHOPM/whgwoQJGDp0KP7yl79A13UsXLgQF1544Z5uAkpLSyEIguXGqa6u9mw3e/ZsPP3009b0ihUr8OGHH1rbK0kSbrzxRhx88MEYPHgwZs+ejTlz5uDZZ591LSccDuOxxx7D2LFjMc7HUnn55Zdj+vTpGDx4MI4++mjcdNNN1nJkWUZRURE4jrP6G4lEsGrVKvzxj3/En//8Zxx55JEYOnQorrzyShxxxBFYsGDBHu8ngiAIgiAIYt9GiBUIzpEThiDyEi6x6zlwvZQOiSCyUST72q3sZXFCEHi0CEyM+GrgOAx84kkAQAaA7Cn8AzzPQQ4aacgOOIT9v4DYkF2vqbecMJIiWEMXQhrrsywV7svwr/4CJbETYz5fWLBtpLrY+rsz6cgIYtAfn8bQf76E8OGH93ZXiD2AhnZ0M1wwiJH//bBX1rs7lJeX45vf/CaeeOIJ6LqOb37zmygvz1/Q04urr74av/zlL5FMJiHLMn72s5/he9/7Xk67Cy+8EAsWLEB9fT3a2towc+ZMPPDAA7vV965y5pln4mc/+xneffddTJkyBU899RQmTJiAMWPGWG0efvhhPPbYY1i3bh3i8ThSqRQmTJjgWs748eMhy/mt6a+//jpuvfVWfPbZZ2hpaUEmk0EikUB7ezvC4bDnd/773/9C13UrxZlJMplEWVnZ7m00QRAEQRAEsc9Te8//YPtDv8WA23+d8xnvfPYkJwxB5CVWZr9X7+tpgoj+g9MJky1W7A0SPBBTgfe/aMSRs8YC+BoZDhDznCNKSEIqriIJBUBHQRGmLeku3O7nsulpOI6DJnIQMjoUI9GY1Il9Hmtdj2+8e12n1hGIKgBaAZAThugcvKJAbmjo7W4QewiJMN0Mx3G7lRasN7nwwgvxgx/8AADw4IMP7tYyfvazn2HOnDkIhUKoqanxTXUwe/ZsXHXVVZg/fz7OO+88iGLuIcjzfE6ar+zUZrtDTU0Npk+fjqeffhpTpkzBH//4R5dQ9Oyzz+InP/kJ7rrrLhx22GGIRqP4zW9+gyVLlriW4yeimKxbtw4zZ87EvHnzcNNNN6G0tBRvvfUW5s6dm3c7NE2DIAj48MMPIWTlyzTTlREEQRAEQRBENrEZMxCbMcPzM6mqClW//CX4UAgcOWEIIi+yIx0ZjVAn+gpO90ssKOVp2TMUFSnA9hRWb2pFMqkCAFQAUp57ipmSLN7GYiBCAVFldHUUn25ssb/fi+efKPPQM6o1rcj5+1L5s59h229+0+nlB8LO6wyJMASxv0AiDIEZM2YglUoBAE444YTdWkZ5eTmGDRtWsF1paSlOPvlkPPvss3j44Yc921RUVGDZsmWueR9//DEkyf9hQ5ZlqKrq+7nJ7NmzcfXVV+Oss87CqlWrcOaZZ1qfvfnmmzj88MNx2WWXWfNWrVpVcJnZfPDBB8hkMrjrrrvAGw8l2SnNvPo7ceJEqKqKbdu24cgjj+zyegmCIAiCIAjCi9JzZvd2FwiiX6A4allICokwRN+gMmanyRtUuvcH/caKFSS3pxDSOKRSLI6R4fT8ThjjXEoYIkwhsWF8bZFLhJF6UZwQBR5pOESYAk6Y2Le+ZYkw1TdcX3D5gbAd2yIRhiD2H+ipgoAgCPj888/x+eef5zgwTJqbm/Hxxx+7/q1fv3631rdw4UJs374do0aN8vz86KOPxgcffIDf//73WLlyJW644YYcUSabhoYGvPHGG9i4cSO2b9/u227WrFloaWnBpZdeiunTp6O2ttb6bNiwYfjggw/w8ssvY8WKFbjuuuvw/vvvd3n7hg4dikwmg/vvvx+rV6/Gk08+mSM4NTQ0oK2tDf/+97+xfft2dHR0YMSIEZg9ezbOO+88PPfcc1izZg3ef/993H777XjxxRe73A+CIAiCIAiCIAii8ygOJ8zGFU291xGCcFAZDeD5yw7HH+YeigMGFqj91QPEjHRok5o4ywmTQaF0ZO4x34WcZU63jyzwvtlV9gaKnFWfRs4vwvCKna5eGekd53It3yHCSAqNjSeI/QUSYQgAQCwWQywW8/180aJFmDhxouvf9dcXVvi9CAaDeWucnHDCCbjuuutw1VVX4eCDD0ZrayvOO++8vMv81a9+hbVr12Lo0KGoqKjwbReLxXDSSSfhk08+wezZ7hGB8+bNw6xZs3DGGWfg0EMPxY4dO1yumM4yYcIE3H333bj99tsxbtw4PPXUU7jttttcbQ4//HDMmzcPZ5xxBioqKnDHHXcAABYsWIDzzjsPV1xxBUaOHImTTz4ZS5YsQV1dXZf7QRAEQRAEQRAEQXQeJWQHRzVVz9OSIPYuE+tLcMTw8l4RJ4qKbCfOlp1xAIDKdS4dmUkhx0ck4HCh9bI7RJTc26VI+UOnvCOWpmcKp9IPRvZ+SjmCIHofTs8uvkG4aGlpQVFREZqbm3NEikQigTVr1mDw4MEIBAK91ENif0LTNLS0tCAWi4HneToGiV4nnU7jxRdfxMyZM/OmDCSIfQE63om+BB2PBOEPnR/E7qLrOn77/UXQNR3fvnwCBo4q3eNl0vFI9He2rmnBX27/AK2cjjHfGoQNf1uPr0UNtz1wrO933vjjl1i6eKM1fewFYzDy0Oqcdub5cehRx2DK7YsBANGAiKXzdy9VfnfwxC/+g7ZdSWv6yB+PxwGj/Qf7AsDm665HcsUKDPrDk+AKnOfLFn+NxX9cAQAYP20gjjpzxJ53mugW6HpNdJV8ukE25HsjCIIgCIIgCIIgCGK/h+M4XPbQ9N7uBkH0KQKGc0PRgdZ2Vk9YK2BWkQLuFF6FnDCxoB3wzvSyC80pwABAQMmfjgwAam76VaeX33BAuSXCHHTioK51jiCIfguJMARBEARBEARBEARBEARB5BAIs9ChDA5rNrWiBJ0QYbJqnQgFasJIjs/z1ZrpDUIhuXCjLhApCWDO7d+AEhQhFqg3QxDEvgPVhCEIgiAIgiAIgiAIgiAIIgc5KEIz/tY6VPb/AtFESemaEwYAThzH0pWdeXDv1sQ98Gj3+kOh7h+/Hi5SSIAhiP0McsIQBEEQBEEQBEEQBEEQBJEDx3HgOAA6IO9g6cgyfH5RpavpyADgodmT0JrMIBbo3VocctDd91CQQqcEQew55IQhCIIgCIIgCIIgCIIgCMITzijTEuxgnhi1gIkjxwkjFg4/chzX6wIMwJw/TgISiTAEQew5JMIQBEEQBEEQBEEQBEEQBOGJNDwKABANMUYt4GyRA27hojNOmL5Cdt8JgiC6AxJhCIIgCIIgCIIgCIIgCILwJJglTKhiIRHG7YQRhP4TfuQLbBtBEMTu0H+uggRBEARBEARBEARBEARB7FW0rQnXNFcgmpidfqw/OWFqhhZZfx97wZhe7AlBEPsS/UKEWbt2LebOnYvBgwcjGAxi6NChuOGGG5BKpfJ+b86cOUYBMfvflClT9lKvCYIgCIIgCIIgCIIgCKJ/U1wdck0LXH5Rpbw24pruTyJMUYW9rU5BhiAIYk/oF4kOv/jiC2iahkceeQTDhg3DsmXLcPHFF6O9vR133nln3u/OmDEDCxYssKZlWe7p7hIEQRAEQRAEQRAEQRDEPsFBJwzC15/usKY1Kb+oIkjuMd9SVnqyvs65Nx+GeGsasfJgb3eFIIh9hH7hhDGFlOOPPx5DhgzBySefjCuvvBLPPfdcwe8qioLq6mrrX2lp6V7ocd/HdAn9+te/ds1/4YUXwBkjGhYtWgSO49DU1JTz/YaGBtxzzz050+Z38v1buHAhAOCRRx7BgQceiHA4jOLiYkycOBG33357T20yQRAEQRAEQRAEQRAE0UUGNMRc0+2VSpe+HwhL3dmdHidWHkTV4FjhhgRBEJ2kXzhhvGhubu6UoLJo0SJUVlaiuLgYU6dOxS233ILKykrf9slkEslk0ppuaWkBAKTTaaTTaVfbdDoNXdehaRo0TdvNLekddF1HIBDA7bffjosvvhglJSUAYG2Hc5v8ts/cduf0lClTsHHjRmve5ZdfjpaWFjz++OPWvKKiIjz66KP46U9/invuuQdTp05FMpnEp59+is8//7zf7cu9ia7r1v/N30XXdaTTaQhC/xpZQuwbmNfF7OsjQeyL0PFO9CXoeCQIf+j8IPoSdDwS+xptnI5IUCx4TM/8/ji8+OAyyEEBnKB7tqfzg+hL0PFIdJWuHCv9UoRZtWoV7r//ftx1111525144ok4/fTTMWjQIKxZswbXXXcdjj76aHz44YdQFG/V/rbbbsONN96YM/+VV15BKOTOgSmKIqqrq9HW1mbVp9F1HWp674sIgsRbDpbOkE6nMXXqVKxZswY33ngjfvWrXwEA4vE4ACY+dXR0AABaW1vB827TlKZpSCQSlkhlTicSCdd+EkURgiC45qXTabzwwgs45ZRTcPrpp1vz6+rq8M1vftNaJuFPa2srACCVSiEej+ONN95AJpPp5V4R+zOvvvpqb3eBIPYadLwTfQk6HgnCHzo/iL4EHY9E/ycKANABNDVuxosvbszfHMDAE9n/X3zxxbzt6Pwg+hJ0PBKdxYydd4ZeFWHmz5/vKXg4ef/99zF58mRretOmTZgxYwZOP/10XHTRRXm/e8YZZ1h/jxs3DpMnT8agQYPwj3/8A7NmzfL8zi9+8Qv89Kc/taZbWlpQV1eH448/HrGY24qYSCSwYcMGRCIRBAIBAEA6qeKxX7yZt189wUX/cyQkpfNOCEm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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "Y_plot = Y_hat_df[Y_hat_df['unique_id']=='OT'] # OT dataset\n",
    "cutoffs = Y_hat_df['cutoff'].unique()[::horizon]\n",
    "Y_plot = Y_plot[Y_hat_df['cutoff'].isin(cutoffs)]\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(Y_plot['ds'], Y_plot['y'], label='True')\n",
    "for model in models:\n",
    "    plt.plot(Y_plot['ds'], Y_plot[f'{model}'], label=f'{model}')\n",
    "plt.xlabel('Datestamp')\n",
    "plt.ylabel('OT')\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we compute the test errors using the Mean Absolute Error (MAE) and Mean Squared Error (MSE):\n",
    "\n",
    "$\\qquad MAE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} |y_{\\tau} - \\hat{y}_{\\tau}| \\qquad$ and $\\qquad MSE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} (y_{\\tau} - \\hat{y}_{\\tau})^{2} \\qquad$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TSMixer horizon 96 - MAE: 0.250\n",
      "TSMixer horizon 96 - MSE: 0.163\n",
      "TSMixerx horizon 96 - MAE: 0.257\n",
      "TSMixerx horizon 96 - MSE: 0.170\n",
      "MLPMultivariate horizon 96 - MAE: 0.322\n",
      "MLPMultivariate horizon 96 - MSE: 0.257\n",
      "NHITS horizon 96 - MAE: 0.251\n",
      "NHITS horizon 96 - MSE: 0.179\n"
     ]
    }
   ],
   "source": [
    "from neuralforecast.losses.numpy import mse, mae\n",
    "\n",
    "for model in models:\n",
    "    mae_model = mae(Y_hat_df['y'], Y_hat_df[f'{model}'])\n",
    "    mse_model = mse(Y_hat_df['y'], Y_hat_df[f'{model}'])\n",
    "    print(f'{model} horizon {horizon} - MAE: {mae_model:.3f}')\n",
    "    print(f'{model} horizon {horizon} - MSE: {mse_model:.3f}')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For reference, we can check the performance when compared to self-reported performance in the paper. We find that `TSMixer` provides better results than the _univariate_ method `NHITS`. Also, our implementation of `TSMixer` very closely tracks the results of the original paper. Finally, it seems that there is little benefit of using the additional exogenous variables contained in the dataframe `X_df` as `TSMixerx` performs worse than `TSMixer`, especially on longer horizons. Note also that `MLPMultivariate` clearly underperforms as compared to the other methods, which can be somewhat expected given its relative simplicity.\n",
    "\n",
    "Mean Absolute Error (MAE)\n",
    "\n",
    "| Horizon   |TSMixer<br> (this notebook) | TSMixer <br>(paper) | TSMixerx<br> (this notebook) |  NHITS <br>(this notebook)    | NHITS <br>(paper)  | MLPMultivariate <br>(this notebook) \n",
    "|---        |---        |---        |---        |---        |---        |---\n",
    "|  96       | **0.250** | 0.252     | 0.257     |  0.251    |   0.255   | 0.322      \n",
    "|  192      | **0.288** | 0.290     | 0.300     |  0.291    |   0.305   | 0.361\n",
    "|  336      | **0.323** | 0.324     | 0.380     |  0.344    |   0.346   | 0.390 \n",
    "|  720      | **0.377** | 0.422     | 0.464     |  0.417    |   0.413   | 0.608\n",
    "\n",
    "Mean Squared Error (MSE)\n",
    "\n",
    "| Horizon   |TSMixer<br> (this notebook) | TSMixer <br>(paper) | TSMixerx<br> (this notebook) | NHITS <br>(this notebook)    | NHITS <br>(paper) | MLPMultivariate <br>(this notebook) \n",
    "|---        |---        |---            |---        |---                |---        |---            \n",
    "|  96       | **0.163** | **0.163**     | 0.170     |  0.179            |   0.176   | 0.255\n",
    "|  192      | 0.220     | **0.216**     | 0.231     |  0.239            |   0.245   | 0.330  \n",
    "|  336      | 0.272     | **0.268**     | 0.361     |  0.311            |   0.295   | 0.376  \n",
    "|  720      | **0.356** | 0.420         | 0.493     |  0.451            |   0.401   | 3.421 \n",
    "\n",
    "Note that for the table above, we use the same hyperparameters for all methods for all horizons, whereas the original papers tune the hyperparameters for each horizon."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Tuning the hyperparameters\n",
    "The `AutoTSMixer` / `AutoTSMixerx` class will automatically perform hyperparamter tunning using the [Tune library](https://docs.ray.io/en/latest/tune/index.html), exploring a user-defined or default search space. Models are selected based on the error on a validation set and the best model is then stored and used during inference. \n",
    "\n",
    "The `AutoTSMixer.default_config` / `AutoTSMixerx.default_config`  attribute contains a suggested hyperparameter space. Here, we specify a different search space following the paper's hyperparameters. Feel free to play around with this space.\n",
    "\n",
    "For this example, we will optimize the hyperparameters for `horizon = 96`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray import tune\n",
    "from ray.tune.search.hyperopt import HyperOptSearch\n",
    "from neuralforecast.auto import AutoTSMixer, AutoTSMixerx\n",
    "\n",
    "horizon = 96 # 24hrs = 4 * 15 min.\n",
    "\n",
    "tsmixer_config = {\n",
    "       \"input_size\": input_size,                                                 # Size of input window\n",
    "       \"max_steps\": tune.choice([500, 1000, 2000]),                              # Number of training iterations\n",
    "       \"val_check_steps\": 100,                                                   # Compute validation every x steps\n",
    "       \"early_stop_patience_steps\": 5,                                           # Early stopping steps\n",
    "       \"learning_rate\": tune.loguniform(1e-4, 1e-2),                             # Initial Learning rate\n",
    "       \"n_block\": tune.choice([1, 2, 4, 6, 8]),                                  # Number of mixing layers\n",
    "       \"dropout\": tune.uniform(0.0, 0.99),                                       # Dropout\n",
    "       \"ff_dim\": tune.choice([32, 64, 128]),                                     # Dimension of the feature linear layer\n",
    "       \"scaler_type\": 'identity',       \n",
    "    }\n",
    "\n",
    "tsmixerx_config = tsmixer_config.copy()\n",
    "tsmixerx_config['futr_exog_list'] = ['ex_1', 'ex_2', 'ex_3', 'ex_4']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To instantiate `AutoTSMixer` and `AutoTSMixerx` you need to define:\n",
    "\n",
    "* `h`: forecasting horizon\n",
    "* `n_series`: number of time series in the multivariate time series problem.\n",
    "\n",
    "In addition, we define the following parameters (if these are not given, the `AutoTSMixer`/`AutoTSMixerx` class will use a pre-defined value):\n",
    "* `loss`: training loss. Use the `DistributionLoss` to produce probabilistic forecasts.\n",
    "* `config`: hyperparameter search space. If `None`, the `AutoTSMixer` class will use a pre-defined suggested hyperparameter space.\n",
    "* `num_samples`: number of configurations explored. For this example, we only use a limited amount of `10`.\n",
    "* `search_alg`: type of search algorithm used for selecting parameter values within the hyperparameter space.\n",
    "* `backend`: the backend used for the hyperparameter optimization search, either `ray` or `optuna`. \n",
    "* `valid_loss`: the loss used for the validation sets in the optimization procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = AutoTSMixer(h=horizon,\n",
    "                    n_series=7,\n",
    "                    loss=MAE(),\n",
    "                    config=tsmixer_config,\n",
    "                    num_samples=10,\n",
    "                    search_alg=HyperOptSearch(),\n",
    "                    backend='ray',\n",
    "                    valid_loss=MAE())\n",
    "\n",
    "modelx = AutoTSMixerx(h=horizon,\n",
    "                    n_series=7,\n",
    "                    loss=MAE(),\n",
    "                    config=tsmixerx_config,\n",
    "                    num_samples=10,\n",
    "                    search_alg=HyperOptSearch(),\n",
    "                    backend='ray',\n",
    "                    valid_loss=MAE())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we fit the model by instantiating a `NeuralForecast` object with the following required parameters:\n",
    "\n",
    "* `models`: a list of models.\n",
    "\n",
    "* `freq`: a string indicating the frequency of the data. (See [panda's available frequencies](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).)\n",
    "\n",
    "The `cross_validation` method allows you to simulate multiple historic forecasts, greatly simplifying pipelines by replacing for loops with `fit` and `predict` methods.\n",
    "\n",
    "With time series data, cross validation is done by defining a sliding window across the historical data and predicting the period following it. This form of cross validation allows us to arrive at a better estimation of our model’s predictive abilities across a wider range of temporal instances while also keeping the data in the training set contiguous as is required by our models.\n",
    "\n",
    "The `cross_validation` method will use the validation set for hyperparameter selection, and will then produce the forecasts for the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-22 09:08:28,183\tINFO worker.py:1724 -- Started a local Ray instance.\n",
      "2024-03-22 09:08:29,427\tINFO tune.py:220 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call `ray.init(...)` before `Tuner(...)`.\n",
      "2024-03-22 09:08:29,429\tINFO tune.py:583 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n",
      "2024-03-22 09:08:45,570\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:09:03,688\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:09:17,107\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:09:28,650\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:09:47,489\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:10:19,949\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:11:20,191\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:11:30,224\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:12:06,451\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:12:28,275\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n",
      "2024-03-22 09:13:12,831\tINFO tune.py:583 -- [output] This uses the legacy output and progress reporter, as Jupyter notebooks are not supported by the new engine, yet. For more information, please see https://github.com/ray-project/ray/issues/36949\n",
      "2024-03-22 09:13:42,119\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:14:08,067\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:14:34,340\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:14:59,946\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:16:44,930\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:17:02,576\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:17:22,409\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:17:41,035\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:18:02,149\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "2024-03-22 09:19:45,156\tINFO tensorboardx.py:275 -- Removed the following hyperparameter values when logging to tensorboard: {'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'), 'loss': ('__ref_ph', 'de895953'), 'valid_loss': ('__ref_ph', '004b9a7a')}\n",
      "INFO:lightning_fabric.utilities.seed:Seed set to 1\n"
     ]
    }
   ],
   "source": [
    "%%capture\n",
    "nf = NeuralForecast(models=[model, modelx], freq='15min')\n",
    "Y_hat_df = nf.cross_validation(df=Y_df, val_size=val_size,\n",
    "                               test_size=test_size, n_windows=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Evaluate Results\n",
    "\n",
    "The `AutoTSMixer`/`AutoTSMixerx` class contains a `results` attribute that stores information of each configuration explored. It contains the validation loss and best validation hyperparameter. The result dataframe `Y_hat_df` that we obtained in the previous step is based on the best config of the hyperparameter search. For `AutoTSMixer`, the best config is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_size': 512,\n",
       " 'max_steps': 2000,\n",
       " 'val_check_steps': 100,\n",
       " 'early_stop_patience_steps': 5,\n",
       " 'learning_rate': 0.0008831625975972278,\n",
       " 'n_block': 1,\n",
       " 'dropout': 0.531963627534685,\n",
       " 'ff_dim': 128,\n",
       " 'scaler_type': 'identity',\n",
       " 'n_series': 7,\n",
       " 'h': 96,\n",
       " 'loss': MAE(),\n",
       " 'valid_loss': MAE()}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf.models[0].results.get_best_result().config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and for `AutoTSMixerx`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_size': 512,\n",
       " 'max_steps': 500,\n",
       " 'val_check_steps': 100,\n",
       " 'early_stop_patience_steps': 5,\n",
       " 'learning_rate': 0.006813015000503828,\n",
       " 'n_block': 1,\n",
       " 'dropout': 0.6915259307542235,\n",
       " 'ff_dim': 32,\n",
       " 'scaler_type': 'identity',\n",
       " 'futr_exog_list': ('ex_1', 'ex_2', 'ex_3', 'ex_4'),\n",
       " 'n_series': 7,\n",
       " 'h': 96,\n",
       " 'loss': MAE(),\n",
       " 'valid_loss': MAE()}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nf.models[1].results.get_best_result().config"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute the test errors of the best config for the two metrics of interest:\n",
    "\n",
    "$\\qquad MAE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} |y_{\\tau} - \\hat{y}_{\\tau}| \\qquad$ and $\\qquad MSE = \\frac{1}{Windows * Horizon} \\sum_{\\tau} (y_{\\tau} - \\hat{y}_{\\tau})^{2} \\qquad$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE TSMixer: 0.250\n",
      "MSE TSMixer: 0.163\n",
      "MAE TSMixerx: 0.264\n",
      "MSE TSMixerx: 0.178\n"
     ]
    }
   ],
   "source": [
    "y_true = Y_hat_df.y.values\n",
    "y_hat_tsmixer = Y_hat_df['AutoTSMixer'].values\n",
    "y_hat_tsmixerx = Y_hat_df['AutoTSMixerx'].values\n",
    "\n",
    "print(f'MAE TSMixer: {mae(y_hat_tsmixer, y_true):.3f}')\n",
    "print(f'MSE TSMixer: {mse(y_hat_tsmixer, y_true):.3f}')\n",
    "print(f'MAE TSMixerx: {mae(y_hat_tsmixerx, y_true):.3f}')\n",
    "print(f'MSE TSMixerx: {mse(y_hat_tsmixerx, y_true):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare the error metrics for our optimized setting to the earlier setting in which we used the default hyperparameters. In this case, for a horizon of 96, we got slightly improved results for `TSMixer` on `MAE`. Interestingly, we did not improve for `TSMixerx` as compared to the default settings. For this dataset, it seems there is limited value in using exogenous features with the `TSMixerx` architecture for a horizon of 96.\n",
    "\n",
    "| Metric    |TSMixer<br> (optimized) | TSMixer <br>(default)  | TSMixer <br>(paper)   |TSMixerx<br> (optimized) | TSMixerx <br>(default) \n",
    "|---        |---                     |---                     |---                    |---                      |---\n",
    "| MAE       | **0.247**              | 0.250                  | 0.252                 | 0.258                   | 0.257 \n",
    "| MSE       | **0.162**              | 0.163                  | 0.163                 | 0.174                   | 0.170\n",
    "\n",
    "Note that we only evaluated 10 hyperparameter configurations (`num_samples=10`), which may suggest that it is possible to further improve forecasting performance by exploring more hyperparameter configurations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "[Chen, Si-An, Chun-Liang Li, Nate Yoder, Sercan O. Arik, and Tomas Pfister (2023). \"TSMixer: An All-MLP Architecture for Time Series Forecasting.\"](http://arxiv.org/abs/2303.06053) <br>\n",
    "[Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
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